Steering lamp control method, device, apparatus and medium for vehicle

Abstract

The application discloses a kind of vehicle's steering light control method, device, equipment and medium.The method comprises: after obtaining the steering light opening instruction of user, the current driving data of target vehicle, current driving road image and the historical driving data of user are obtained;According to the historical driving data of user and the user driving behavior corresponding to historical driving data, the historical steering determination model of user is determined;According to the current driving data of target vehicle, current driving road image and the historical steering determination model of user, the current steering state of target vehicle is determined, and the steering light is controlled according to current steering state and is closed.When steering light is opened, the steering state of vehicle is judged according to the current driving data of vehicle and current driving road image using user historical steering determination model, and the steering light is controlled according to steering state, realizes the automatic control to steering light, improves the use vehicle experience of user.

Description

Technical Field

[0001] This invention relates to the field of unmanned vehicle technology, and in particular to a method, device, equipment and medium for controlling the turn signals of a vehicle. Background Technology

[0002] Vehicle development is gradually shifting towards automation and humanization, especially by upgrading traditional vehicle components to new models that support automated functions to improve the user experience. Turn signals are traditional vehicle components, and their automation upgrades are receiving considerable attention.

[0003] Currently, most vehicle manufacturers control turn signals based on whether the steering wheel angle has returned to center. Because this is a single, inefficient method, turn signals often turn off prematurely. While Tesla has updated all its vehicles with an "Intelligent Turn Signal Off" feature via over-the-air (OTA) update, this feature primarily uses image recognition, but its control functionality remains imperfect. Summary of the Invention

[0004] This invention provides a method, device, equipment, and medium for controlling vehicle turn signals to achieve intelligent control of turn signals.

[0005] According to one aspect of the present invention, a method for controlling the turn signals of a vehicle is provided, the method comprising:

[0006] After receiving the user's turn signal activation command, the system acquires the target vehicle's current driving data, the current road image, and the user's historical driving data.

[0007] Based on the user's historical driving data and the user's driving behavior corresponding to the historical driving data, a historical steering determination model for the user is determined.

[0008] Based on the target vehicle's current driving data, the current road image, and the user's historical steering determination model, the current steering state of the target vehicle is determined, and the turn signal is turned off according to the current steering state.

[0009] According to another aspect of the present invention, a turn signal control device for a vehicle is provided, the device comprising:

[0010] Target vehicle data acquisition module: After receiving the user's turn signal activation command, it acquires the target vehicle's current driving data, the current road image, and the user's historical driving data;

[0011] Historical steering determination model determination module: used to determine the user's historical steering determination model based on the user's historical driving data and the user's driving behavior corresponding to the historical driving data;

[0012] Turn signal control module: used to determine the current turning state of the target vehicle based on the target vehicle's current driving data, the current driving road image, and the user's historical turning determination model, and control the turn signal to turn off according to the current turning state.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] At least one processor; and

[0015] A memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the vehicle turn signal control method according to any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the vehicle turn signal control method according to any embodiment of the present invention.

[0018] The technical solution of this invention receives the target vehicle's current driving data and the current road image based on the user's command to activate the turn signal, avoiding the reception of other data generated during vehicle operation. It uses the user's historical driving data as input and the corresponding user driving behavior as output to determine a historical steering determination model, ensuring the accuracy of the model. By inputting the vehicle's current driving state and the current road image into the model, the current steering state is determined, enabling the vehicle to automatically determine its current steering state and control the turn signal to turn off based on the current driving state, thus achieving automated turn signal control and improving the user's driving experience.

[0019] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart of a turn signal control method provided in Embodiment 1 of the present invention;

[0022] Figure 2 A flowchart of another turn signal control method provided in Embodiment 2 of the present invention;

[0023] Figure 3 A flowchart of another turn signal control method provided in Embodiment 3 of the present invention;

[0024] Figure 4 A flowchart of another turn signal control method provided in Embodiment 3 of the present invention;

[0025] Figure 5 This is a schematic diagram of the structure of a vehicle turn signal control device provided in Embodiment 4 of the present invention;

[0026] Figure 6 A schematic diagram of the structure of an electronic device for implementing the vehicle turn signal control method of this embodiment of the invention. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "candidate," "target," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0029] Example 1

[0030] Figure 1This is a flowchart of a vehicle turn signal control method provided in Embodiment 1 of the present invention. This embodiment is applicable to situations where turn signals are automatically turned on and off. The method can be executed by a vehicle turn signal control device, which can be implemented in hardware and / or software and can be configured in the vehicle terminal. Figure 1 As shown, the method includes:

[0031] S110. After receiving the user's turn signal activation command, obtain the target vehicle's current driving data, the current road image, and the user's historical driving data.

[0032] The target vehicle can be the vehicle currently under the user's control.

[0033] Current driving data can be data generated during the vehicle's current driving process that characterizes the vehicle's current driving status. This data may include vehicle identification number (VIN), engine status, vehicle interior temperature, current fuel level, vehicle model, and window lighting information. The VIN is a 17-digit string of letters and numbers containing important information about the vehicle, and each VIN uniquely corresponds to a single vehicle. One current driving data point is collected every 10 seconds.

[0034] The current driving road image can be image information of the road environment surrounding the vehicle's current driving segment, captured by an image acquisition device. This image information includes views of the road in front of, to the left of, to the right of, and behind the vehicle. The acquisition device can be any device capable of capturing image information, such as a camera.

[0035] A user's historical driving data can be the driving data generated by the vehicle when the user performed steering operations in the past, which can be received by the vehicle's onboard terminal.

[0036] When a user activates the turn signal, the vehicle's ECU (Electronic Control Unit) receives the command and begins recording the vehicle's current driving data every 10 seconds, while also retrieving the user's historical driving data from a cloud database. The cloud database stores the vehicle's historical driving data.

[0037] In the above steps, a cloud database is used to store the vehicle's historical driving information, which realizes automatic data backup and facilitates data management.

[0038] Optionally, driving data may include at least: steering wheel angle direction, steering wheel angle size, timestamp, vehicle speed information, vehicle mileage value, and vehicle location information.

[0039] A timestamp can be a sequence of characters or encoded information used to identify when a specific event occurs. It usually gives the date and time, and sometimes it is accurate to the second. In this application, the time of the timestamp is accurate to the second.

[0040] The vehicle's location information can be the location of the vehicle on the road segment it is currently traveling on, which can be represented by longitude and latitude values.

[0041] For example, the driving data corresponding to a vehicle changing lanes includes the following content as shown in the table below.

[0042] Table 1

[0043]

[0044] S120. Based on the user's historical driving data and the user's driving behavior corresponding to the historical driving data, determine the user's historical steering determination model.

[0045] User driving behavior is used to characterize the current user's steering actions, which include changing lanes, turning, or making a U-turn. For example, a user's driving behavior could be changing lanes, turning, or making a U-turn.

[0046] User driving behavior is obtained by analyzing and calibrating their historical driving data. This can be done manually or using calibration tools, such as the pandas library in Python. For example, based on changes in steering wheel angle, angle size, and vehicle speed, it can be determined whether the user is changing lanes, turning, or making a U-turn. A change in steering wheel angle within 10° is considered a lane change; a change greater than 90° is considered a U-turn; and a change within 90° is considered a turn. Timestamps can be used to determine the start and end times of the turn.

[0047] A personal driving habit database for each driver is established based on the user's historical driving data and the corresponding driving behavior.

[0048] By adopting the above steps and establishing a driver's driving habit database using historical data, the accuracy of model judgments can be improved.

[0049] The user's historical turn determination model can be a model that judges the current data by sorting through massive amounts of historical data, such as a fuzzy controller or a neural network model.

[0050] Based on the user's historical driving data and the relationship between the user's driving behavior corresponding to the historical driving data, a historical steering determination model for the user is established.

[0051] By adopting the above steps, a historical steering determination model is established based on the historical information of the target vehicle's users. This enables the creation of a model suitable for different drivers based on their driving habits, thereby improving the accuracy of the historical steering determination model.

[0052] S130. Based on the target vehicle's current driving data, the current road image, and the user's historical steering determination model, determine the target vehicle's current steering state, and control the turn signal to turn off according to the current steering state.

[0053] The current steering status indicates whether the vehicle has completed a lane change, turn, or U-turn. For example, the current steering status can be "steering complete" or "steering in progress." "Steering complete" means that the vehicle has completed a lane change, a turn, or a U-turn; "steering in progress" means that the vehicle is changing lanes, turning, or making a U-turn.

[0054] The target vehicle's current driving data and the current road image are used as inputs to the user's historical steering determination model. The target vehicle's current steering state is determined by the user's historical steering determination model. Based on the current steering state, it is determined whether the current vehicle has completed a lane change, steering, or U-turn. If it has, the turn signal is turned off.

[0055] Optionally, control the turn signal to turn off based on the current steering status, including steps A1-A2:

[0056] Step A1: If the current steering status is "steering complete", then turn off the turn signal.

[0057] If the user's historical steering determination model determines that the current vehicle steering status is "steering completed," meaning a lane change, steering, or U-turn has been completed, then the turn signal should be turned off.

[0058] Step A2: If the current steering status is "in the process of turning", keep the turn signal on and continue to determine the current steering status of the target vehicle.

[0059] If the user's historical steering determination model determines that the current vehicle steering state is "steering", it means that the vehicle is currently changing lanes, turning, or making a U-turn. In this case, while keeping the turn signal on, the user's historical steering determination model will continue to be used to determine the current steering state of the target vehicle.

[0060] Optionally, after the turn signal is turned off, the following also includes:

[0061] Stop acquiring the target vehicle's current driving data, current road image, and the user's historical driving data, and save the acquired data to a cloud database.

[0062] This embodiment's technical solution receives the target vehicle's current driving data and the current road image based on the user's command to activate the turn signal, avoiding the reception of other data generated during vehicle operation and ensuring data uniqueness. It uses the user's historical driving data as input and the corresponding user driving behavior as output to determine a historical steering determination model, ensuring the model's accuracy. By inputting the vehicle's current driving state and the current road image into the model, the model determines the vehicle's current steering state, enabling automatic judgment of the vehicle's current steering state and controlling the turn signal's closure based on the current driving state. This achieves automated turn signal control and improves the user's driving experience.

[0063] Example 2

[0064] Figure 2 This is a flowchart of another vehicle turn signal control method provided in Embodiment 2 of the present invention. In this embodiment, the steps, like those in the previous embodiments, determine the expansion of the user's historical steering determination model based on the user's historical driving data and the corresponding user driving behavior. For example... Figure 2 As shown, the method includes:

[0065] S210. After receiving the user's turn signal activation command, obtain the target vehicle's current driving data, the current road image, and the user's historical driving data.

[0066] S220. Determine the historical steering state corresponding to the historical driving data based on the user's driving behavior corresponding to the historical driving data.

[0067] Historical steering status can be either "steering completed" or "steering in progress". Whether a vehicle has completed a steering or U-turn can be determined based on the steering wheel angle, steering direction, and timestamp. For example, if the steering wheel angle changes significantly but the steering direction remains the same, it can be considered as steering in progress. The time required for the vehicle to turn can be obtained from the start and completion times of the steering, and this time can be used to determine whether the vehicle has completed the steering.

[0068] For example, if the user's driving behavior corresponding to the historical driving data is controlling the vehicle's steering, then the historical steering status corresponding to the historical driving data is "steering in progress"; if the user's driving behavior corresponding to the historical driving data is "the vehicle has completed steering", then the historical steering status corresponding to the historical driving data is "steering completed".

[0069] S230. Based on the user's historical driving data and the historical steering states corresponding to the historical driving data, a fuzzy controller is built as the user's historical steering determination model.

[0070] A fuzzy controller is a controller that determines the user's historical steering state based on fuzzy rules during the control of the controlled object. These fuzzy rules can be well-developed language control rules summarized from the driver's driving habits.

[0071] The user's historical driving data is used as input to the fuzzy controller. Fuzzy reasoning is performed according to the fuzzy control rule table to obtain the user's historical steering state. Defuzzification is then performed to obtain the specific user's historical steering state.

[0072] S240. Based on the target vehicle's current driving data, the current road image, and the user's historical steering determination model, determine the target vehicle's current steering state, and control the turn signal to turn off according to the current steering state.

[0073] Optionally, based on the target vehicle's current driving data, the current road image, and the user's historical steering determination model, the current steering state of the target vehicle is determined, including steps B1-B2:

[0074] Step B1: Determine the reference steering state corresponding to the current driving data of the target vehicle based on the current driving data of the target vehicle and the fuzzy controller.

[0075] The reference steering state can be the steering state that may correspond to the current driving data of the target vehicle obtained by inference from the fuzzy controller.

[0076] The current driving data of the target vehicle is used as the input of the fuzzy controller. The fuzzy controller performs fuzzy inference through the set fuzzy rules to obtain the steering state corresponding to the current driving data, and uses the steering state as the reference steering state.

[0077] Step B2: Determine whether to use the reference steering state as the current steering state based on the road information in the current driving road image.

[0078] The road information in the current driving road image can include the position of road signs relative to vehicles, the position of lane lines relative to vehicles, and the position of surrounding buildings and buildings.

[0079] For example, when a vehicle is changing lanes, the judgment can be made based on the position of the sign relative to the vehicle in the image or by the position of the lane line relative to the vehicle; when a vehicle is turning or making a U-turn, the judgment can be made by the changes in the surrounding buildings in the image.

[0080] Based on the road change information in the current driving road image, determine whether the current state of the vehicle is consistent with the reference steering state obtained by the fuzzy controller based on the current driving data. If they are consistent, the reference steering state is taken as the current steering state; if they are inconsistent, continue to compare and judge.

[0081] For example, assuming the vehicle is currently changing lanes, the current steering state is determined by the changes in the image of the road being traveled, captured by the image acquisition device, to determine whether the lane change is in progress or completed. For instance, before changing lanes, the image acquisition device captures a road sign ahead of the vehicle located to its right, and its position relative to the vehicle remains constant in the image. However, when the vehicle begins to change lanes, the position of the sign relative to the vehicle changes in the captured image, indicating that the vehicle is in progress changing lanes. If the fuzzy controller determines the reference steering state based on the current driving data as being in progress changing lanes, then this reference steering state is taken as the current steering state. If the relative position of the sign in the captured image no longer changes, then the lane change is considered complete. If the fuzzy controller determines the reference steering state based on the current driving data as being complete changing lanes, then this reference steering state is taken as the current steering state.

[0082] The above steps improve the accuracy of judging the driver's driving intention by determining the current steering state based on the road information in the current driving road image and the reference steering state obtained by the fuzzy controller based on the current vehicle driving data.

[0083] This embodiment's technical solution establishes a fuzzy controller based on the user's historical driving data and the corresponding historical steering states. The fuzzy controller determines the vehicle's current steering state based on the current vehicle's driving data and compares this with road information in the images captured by the image acquisition device to determine the final vehicle steering state. Compared to methods that directly use images for judgment, this method improves the accuracy of the judgment. By controlling the turn signals according to the vehicle's steering state, the turn signals are automatically turned off, improving the user experience.

[0084] Example 3

[0085] Figure 3 This is a flowchart of another vehicle turn signal control method provided in Embodiment 3 of the present invention. In this embodiment, the steps, like those in the previous embodiments, determine the expansion of the user's historical steering determination model based on the user's historical driving data and the corresponding user driving behavior. For example... Figure 3 As shown, the method includes:

[0086] S310. After receiving the user's turn signal activation command, obtain the target vehicle's current driving data, the current road image, and the user's historical driving data.

[0087] S320. The user's historical driving data is used as input, and the user's driving behavior corresponding to the historical driving data is used as output to train a deep learning model. The trained deep learning model is used as the historical steering determination model.

[0088] Deep learning allows computers to simulate human cognitive processes, learning from experience and understanding the world through a hierarchical conceptual framework, where each concept is defined by its relationship to certain simpler concepts. Deep learning models can employ convolutional neural networks, recurrent neural networks, recurrent neural networks, generative adversarial networks, etc. This invention does not limit the type of deep learning model.

[0089] Specifically, the user's historical driving data is input into the deep learning model. Based on the user's driving behavior output by the deep learning model and the user's driving behavior corresponding to the historical driving data, the accuracy of the deep learning model is determined. The deep learning model is iteratively trained until the accuracy of the deep learning model is greater than or equal to a preset accuracy threshold. The training of the deep learning model is then stopped, and the trained deep learning model is used as the historical steering determination model.

[0090] S330: Based on the target vehicle's current driving data, the current road image, and the user's historical steering determination model, determine the target vehicle's current steering state, and control the turn signal to turn off according to the current steering state.

[0091] Optionally, based on the target vehicle's current driving data, the current road image, and the user's historical steering determination model, the current steering state of the target vehicle is determined, including steps C1-C2:

[0092] Step C1: Input the current driving data of the target vehicle into the historical steering determination model to obtain the first steering state.

[0093] The first steering state can be the vehicle's steering state determined by a deep learning model based on the target vehicle's current driving data.

[0094] The current driving data of the target vehicle is used as input and fed into the historical steering determination model. The model calculates and obtains the first steering state.

[0095] Step C2: Input the current driving data and current road image of the target vehicle into the pre-trained second steering determination model to obtain the second steering state; wherein the second steering determination model is trained based on historical driving data, historical road images, and user driving behavior corresponding to the historical driving data.

[0096] Historical driving data and images of historical driving roads are input into a deep learning model. Based on the user's driving behavior output by the deep learning model and the corresponding user driving behavior in the historical driving data, the accuracy of the deep learning model is determined. The deep learning model is iteratively trained until its accuracy is greater than or equal to a preset accuracy threshold. Training then stops, and the trained deep learning model is used as the second steering determination model. The target vehicle's current driving data and the current driving road image are input into the second steering determination model, and the second steering state is calculated.

[0097] Step C3: Determine the current steering state based on the first steering state and the second steering state.

[0098] If the first steering state and the second steering state are the same, then the steering state is taken as the current steering state; if they are different, then the judgment continues.

[0099] For example, such as Figure 4 As shown, when the user turns on the turn signal, the ECU obtains the vehicle's current driving data, the current road image captured by the camera, and the user's historical driving data. Based on the user's historical driving data, the deep learning model is trained to obtain a historical steering determination model. The vehicle's current driving data and the current road image captured by the camera are used as inputs to the historical steering determination model to calculate the current vehicle steering state. Based on the steering state, the turn signal is turned off.

[0100] This embodiment employs a historical steering determination model to determine a first steering state based on the vehicle's current driving data, and a second steering determination model to determine a second steering state based on the vehicle's current driving data and the current road image. The two steering states are compared; if they match, the selected second steering state is adopted as the current steering state. This method improves the accuracy of the determination. Controlling the turn signals based on the vehicle's current steering state automatically turns them off, improving the user experience.

[0101] Example 4

[0102] Figure 5 This is a schematic diagram of the structure of a vehicle turn signal control device provided in Embodiment 4 of the present invention. Figure 5 As shown, the device includes:

[0103] Target vehicle data acquisition module 410: After receiving the user's turn signal activation command, it acquires the target vehicle's current driving data, the current driving road image, and the user's historical driving data; wherein, the driving data includes at least: steering wheel turning direction, steering wheel turning angle, timestamp, vehicle speed information, vehicle mileage value, and vehicle location information.

[0104] Historical steering determination model determination module 420: used to determine the user's historical steering determination model based on the user's historical driving data and the user's driving behavior corresponding to the historical driving data;

[0105] Turn signal control module 430: used to determine the current turning state of the target vehicle based on the target vehicle's current driving data, the current driving road image and the user's historical turning determination model, and control the turn signal to be turned off according to the current turning state.

[0106] Optionally, the historical steering determination model determination module 420 includes:

[0107] Historical steering state determination unit: used to determine the historical steering state corresponding to the historical driving data based on the user's driving behavior corresponding to the historical driving data;

[0108] Historical steering determination model determination unit: used to build a fuzzy controller based on the user's historical driving data and the historical steering state corresponding to the historical driving data, as the user's historical steering determination model.

[0109] Optionally, the historical steering determination model determination module 420 includes:

[0110] The user's historical driving data is used as input, and the user's driving behavior corresponding to the historical driving data is used as output to train a deep learning model. The trained deep learning model is then used as the historical steering determination model.

[0111] Optional, the turn signal control module 430 includes:

[0112] Reference steering state determination unit: used to determine the reference steering state corresponding to the current driving data of the target vehicle based on the current driving data of the target vehicle and the fuzzy controller;

[0113] Current steering state determination unit: used to determine whether to use the reference steering state as the current steering state based on the road information in the current driving road image.

[0114] Optional, the turn signal control module 430 includes:

[0115] The current driving data of the target vehicle is input into the historical steering determination model to obtain the first steering state;

[0116] The current driving data of the target vehicle and the current road image are input into a pre-trained second steering determination model to obtain the second steering state; the second steering determination model is trained based on historical driving data, historical road images, and user driving behavior corresponding to the historical driving data.

[0117] The current steering state is determined based on the first steering state and the second steering state.

[0118] Optional, the turn signal control module 430 includes:

[0119] Turn signal off module: Used to control the turn signal to turn off if the current turning state is "turning complete";

[0120] Current steering state determination unit: If the current steering state is "steering", the turn signal is kept on while the current steering state of the target vehicle is determined.

[0121] The vehicle turn signal control device provided in the embodiments of the present invention can execute the vehicle turn signal control method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method.

[0122] The acquisition, storage, use, and processing of data in this application comply with relevant national laws and regulations and do not violate public order and good morals.

[0123] Example 5

[0124] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0125] Figure 6 This is a schematic diagram of an electronic device for implementing the vehicle turn signal control method according to an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0126] like Figure 6As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0127] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0128] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the turn signal control method for a vehicle.

[0129] In some embodiments, the vehicle turn signal control method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the vehicle turn signal control method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the vehicle turn signal control method by any other suitable means (e.g., by means of firmware).

[0130] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific reference products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transferring data and instructions to the storage system, the at least one input device, and the at least one output device.

[0131] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0132] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0133] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0134] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0135] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0136] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0137] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for controlling the turn signals of a vehicle, characterized in that, The method includes: After receiving the user's turn signal activation command, the system acquires the target vehicle's current driving data, the current road image, and the user's historical driving data. Based on the user's historical driving data and the user's driving behavior corresponding to the historical driving data, a historical steering determination model for the user is determined. Based on the target vehicle's current driving data, the current road image, and the user's historical steering determination model, the target vehicle's current steering state is determined, and the turn signal is turned off based on the current steering state. Specifically, the current steering state of the target vehicle is determined based on its current driving data, the current road image, and the user's historical steering determination model, including: The current driving data of the target vehicle is input into the historical steering determination model to obtain the first steering state; The current driving data and current road image of the target vehicle are input into a pre-trained second steering determination model to obtain a second steering state; wherein the second steering determination model is trained based on historical driving data, historical road images, and user driving behavior corresponding to the historical driving data. The current steering state is determined based on the first steering state and the second steering state.

2. The method according to claim 1, characterized in that, Based on the user's historical driving data and the corresponding user driving behavior, a historical steering determination model for the user is determined, including: The historical steering state corresponding to the historical driving data is determined based on the user's driving behavior corresponding to the historical driving data. A fuzzy controller is built based on the user's historical driving data and the historical steering state corresponding to the historical driving data, serving as the user's historical steering determination model.

3. The method according to claim 2, characterized in that, Based on the target vehicle's current driving data, the current road image, and the user's historical steering determination model, the current steering state of the target vehicle is determined, including: Based on the current driving data of the target vehicle and the fuzzy controller, a reference steering state corresponding to the current driving data of the target vehicle is determined; Determine whether to use the reference steering state as the current steering state based on the road information in the current driving road image.

4. The method according to claim 1, characterized in that, Based on the user's historical driving data and the corresponding user driving behavior, a historical steering determination model for the user is determined, including: The user's historical driving data is used as input, and the user's driving behavior corresponding to the historical driving data is used as output to train a deep learning model. The trained deep learning model is then used as the historical steering determination model.

5. The method according to claim 1, characterized in that, Driving data includes at least: steering wheel angle direction, steering wheel angle size, timestamp, vehicle speed information, vehicle mileage value, and vehicle location information.

6. The method according to claim 1, characterized in that, Controlling the turn signal to turn off based on the current steering state includes: If the current steering status is "steering complete", then control the turn signal to turn off; If the current steering state is "in steering", then keep the turn signal on and continue to determine the current steering state of the target vehicle.

7. A turn signal control device for a vehicle, characterized in that, include: Target vehicle data acquisition module: After receiving the user's turn signal activation command, it acquires the target vehicle's current driving data, the current road image, and the user's historical driving data; Historical steering determination model determination module: used to determine the user's historical steering determination model based on the user's historical driving data and the user's driving behavior corresponding to the historical driving data; Turn signal control module: used to determine the current turning state of the target vehicle based on the target vehicle's current driving data, the current driving road image, and the user's historical turning determination model, and control the turn signal to turn off according to the current turning state; Specifically, the turn signal control module is used for: The current driving data of the target vehicle is input into the historical steering determination model to obtain the first steering state; The current driving data and current road image of the target vehicle are input into a pre-trained second steering determination model to obtain a second steering state; wherein the second steering determination model is trained based on historical driving data, historical road images, and user driving behavior corresponding to the historical driving data. The current steering state is determined based on the first steering state and the second steering state.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the turn signal control method for the vehicle according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the turn signal control method for the vehicle as described in any one of claims 1-6.

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