Vehicle high beam and low beam switching method and system, vehicle and storage medium

Abstract

The invention discloses a vehicle high beam and low beam switching method and system, a vehicle and a storage medium, and the vehicle high beam and low beam switching method comprises the steps that vehicle operation data are obtained, and the vehicle operation data comprise vehicle internal data and external environment data; according to the vehicle operation data, a decision result is determined through a pre-trained high beam and low beam switching decision model, and the high beam and low beam switching decision model comprises the corresponding relation between the vehicle operation data and the decision result; according to the decision result, a light switching instruction is determined, and the light switching instruction comprises turning on a high beam or turning off the high beam; and according to the light switching instruction, vehicle light is controlled. According to the method and the device, the technical problem that a self-adaptive high-beam and low-beam illumination system cannot be carried if no camera exists for an economical vehicle model in the related technology is solved.

Description

Technical Field

[0001] The present invention relates to the field of vehicle technology, and in particular to a method and system for switching between high and low beam lights of a vehicle, a vehicle and a storage medium. Background Art

[0002] IHC stands for Adaptive High and Low Beam, a headlamp system that automatically changes high and low beam lighting to adapt to changing driving conditions. This system uses a camera in the rearview mirror of the vehicle to detect vehicles in front, with a detection range of up to 400 meters. When an oncoming vehicle is detected, the system will selectively block the high beam range to avoid the oncoming vehicle, ensuring that the driver has a clear field of vision without affecting the driving of oncoming vehicles. When the system detects that there is no vehicle in front, it will automatically switch back to high beam. The development of this technology has played a great role in the safety of night driving. By reducing the need for drivers to manually switch between high and low beams, it improves driving convenience and safety.

[0003] However, the use of camera recognition relies on hardware support, which increases the cost of the vehicle to a certain extent. For economy models, if they do not have cameras, this function cannot be installed, making it impossible for low-end models to experience it. Summary of the invention

[0004] The embodiments of the present invention provide a method, system, vehicle and storage medium for switching between high and low beams of a vehicle, so as to at least solve the technical problem in the related art that, for economical vehicles, if there is no camera, it is impossible to install an adaptive high and low beam lighting system.

[0005] According to one embodiment of the present invention, a method for switching between high and low beams of a vehicle is provided, comprising: acquiring vehicle operation data, wherein the vehicle operation data includes vehicle internal data and external environment data; determining a decision result based on the vehicle operation data using a pre-trained high and low beam switching decision model, wherein the high and low beam switching decision model includes a correspondence between the vehicle operation data and the decision result; determining a light switching instruction based on the decision result, wherein the light switching instruction includes turning on the high beam or turning off the high beam; and controlling the vehicle lights based on the light switching instruction.

[0006] Optionally, the vehicle internal data includes at least one of the following: vehicle speed, high-precision map traffic flow, and high-precision map road conditions.

[0007] Optionally, the external environment data includes at least one of the following: light intensity, GPS time, and weather.

[0008] Optionally, the high-beam and low-beam switching decision model is constructed based on a back-propagation neural network algorithm.

[0009] Optionally, determining a light switching instruction based on the decision result includes: in response to the decision result indicating that the high beam needs to be turned on, determining the light switching instruction is to turn on the high beam; or, in response to the decision result indicating that the high beam needs to be turned off, determining the light switching instruction is to turn off the high beam.

[0010] Optionally, the training of the high and low beam switching decision model includes: obtaining training data, wherein the training data is collected by a preset collection vehicle, and the training data includes historical vehicle internal data and historical external environment data; based on the training data, training a pre-built model to be trained to obtain the high and low beam switching decision model.

[0011] According to one embodiment of the present invention, a vehicle high-beam and low-beam switching system is also provided, including: an acquisition module, used to acquire vehicle operation data, wherein the vehicle operation data includes vehicle internal data and external environment data; a first determination module, used to determine a decision result based on the vehicle operation data using a high-beam and low-beam switching decision model, wherein the high-beam and low-beam switching decision model includes a correspondence between the vehicle operation data and the decision result; a second determination module, used to determine a light switching instruction based on the decision result, wherein the light switching instruction includes turning on the high beam or turning off the high beam; and a control module, used to control the vehicle lights according to the light switching instruction.

[0012] Optionally, the vehicle internal data acquired by the acquisition module includes at least one of the following: vehicle speed, high-precision map traffic flow, and high-precision map road conditions.

[0013] Optionally, the external environment data acquired by the acquisition module includes at least one of the following: light intensity, GPS time, and weather.

[0014] Optionally, the high-beam and low-beam switching decision model in the first determination module is constructed based on a back-propagation neural network algorithm.

[0015] Optionally, the second determination module is further used to: in response to the decision result indicating that the high beam needs to be turned on, determine the light switching instruction to turn on the high beam; or, in response to the decision result indicating that the high beam needs to be turned off, determine the light switching instruction to turn off the high beam.

[0016] Optionally, the training of the high and low beam switching decision model in the first determination module includes: obtaining training data, wherein the training data is collected by a preset collection vehicle, and the training data includes historical vehicle internal data and historical external environment data; according to the training data, training the pre-built model to be trained to obtain the high and low beam switching decision model.

[0017] According to one embodiment of the present invention, there is also provided an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute any of the above-mentioned methods for switching between high and low beam lights of a vehicle.

[0018] According to one embodiment of the present invention, a non-volatile storage medium is further provided, in which a computer program is stored, wherein the computer program is configured to execute any of the above-mentioned vehicle high and low beam switching methods when running.

[0019] According to one embodiment of the present invention, a computer program product is also provided, including a computer program, and when the computer program is executed by a processor, the computer program implements any of the above-mentioned methods for switching between high and low beam lights of a vehicle.

[0020] In an embodiment of the present invention, vehicle operation data is obtained, wherein the vehicle operation data includes vehicle internal data and external environment data; according to the vehicle operation data, a pre-trained high and low beam switching decision model is used to determine a decision result, wherein the high and low beam switching decision model includes a corresponding relationship between the vehicle operation data and the decision result; according to the decision result, a light switching instruction is determined, wherein the light switching instruction includes turning on the high beam or turning off the high beam; according to the light switching instruction, the vehicle light is controlled. The present invention comprehensively analyzes the internal data and external environment data of the vehicle, and the data can be obtained without the vehicle being additionally loaded with a camera. The pre-trained decision model is used to automatically judge and switch the high and low beams, thereby effectively improving the safety and comfort of night driving. The method not only reduces the operating burden of the driver, but also avoids the interference of the misuse of the high beam to other road users through intelligent decision-making, improves the safety of the overall road environment, and can solve the technical problem in the related technology that, for economical models, if there is no camera, the adaptive high and low beam lighting system cannot be equipped. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0022] Figure 1 is a flow chart of a method for switching between high and low beam lights of a vehicle according to one embodiment of the present invention;

[0023] Figure 2 1 is a structural block diagram of a vehicle high and low beam switching system according to one embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings 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 should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] According to an embodiment of the present invention, an embodiment of a method for switching between high and low beam headlights of a vehicle is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system comprising at least one set of computer executable instructions, and, although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0027] The method embodiment can also be executed in an electronic device including a memory and a processor, a similar control device or a vehicle-mounted terminal. Taking a vehicle-mounted terminal as an example, the vehicle-mounted terminal may include one or more processors and a memory for storing data. Optionally, the vehicle-mounted terminal may also include a communication device and a display device for communication functions. It can be understood by those of ordinary skill in the art that the above structural description is only illustrative and does not limit the structure of the vehicle-mounted terminal. For example, the vehicle-mounted terminal may also include more or fewer components than the above structural description, or have a configuration different from the above structural description.

[0028] The processor may include one or more processing units. For example, the processor may include a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processing (DSP) chip, a microcontroller unit (MCU), a field-programmable gate array (FPGA), a neural-network processing unit (NPU), a tensor processing unit (TPU), an artificial intelligence (AI) type processor, and other processing devices. Among them, different processing units may be independent components or integrated into one or more processors. In some instances, the electronic device may also include one or more processors.

[0029] The memory can be used to store computer programs, for example, to store the computer program corresponding to the vehicle high and low beam switching method in the embodiment of the present invention. The processor implements the above-mentioned vehicle high and low beam switching method by running the computer program stored in the memory. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the electronic device via a grid. Examples of the above-mentioned grid include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0030] The communication device is used to receive or send data via a grid. The specific examples of the above-mentioned grid may include a wireless grid provided by a communication provider of the mobile terminal. In one example, the communication device includes a grid adapter (network interface controller, NIC), which can be connected to other grid devices through a base station so as to communicate with the Internet. In one example, the communication device can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly. In some embodiments of the present solution, the communication device is used to connect to mobile devices such as mobile phones and tablets, and instructions can be sent to the vehicle-mounted terminal through the mobile device.

[0031] The display device may be a touch screen type liquid crystal display (LCD) and a touch display (also referred to as a "touch screen" or "touch display screen"). The liquid crystal display may enable a user to interact with a user interface of the vehicle-mounted terminal. In some embodiments, the vehicle-mounted terminal has a graphical user interface (GUI), and a user may interact with the GUI by finger contact and / or gestures on a touch-sensitive surface. The human-computer interaction function here may include a vehicle gear shifting function, and executable instructions for executing the human-computer interaction function are configured / stored in a computer program product or a readable storage medium executable by one or more processors.

[0032] Figure 1 FIG. 1 is a flow chart of a method for switching between high and low beam lights of a vehicle according to one embodiment of the present invention. Figure 1 As shown, the method comprises the following steps:

[0033] Step S101, obtaining vehicle operation data, wherein the vehicle operation data includes vehicle internal data and external environment data.

[0034] It should be noted that the vehicle operation data is obtained based on the vehicle-road cloud data, and there is no need for the vehicle to be equipped with additional cameras for collection.

[0035] Step S102, determining a decision result based on the vehicle operation data and using a pre-trained high-beam and low-beam switching decision model, wherein the high-beam and low-beam switching decision model includes a correspondence between the vehicle operation data and the decision result.

[0036] Step S103: determining a light switching instruction according to the decision result, wherein the light switching instruction includes turning on the high beam or turning off the high beam.

[0037] Step S104, controlling the vehicle lights according to the light switching instruction.

[0038] In an embodiment of the present invention, vehicle operation data is obtained, wherein the vehicle operation data includes vehicle internal data and external environment data; according to the vehicle operation data, a pre-trained high and low beam switching decision model is used to determine a decision result, wherein the high and low beam switching decision model includes a corresponding relationship between the vehicle operation data and the decision result; according to the decision result, a light switching instruction is determined, wherein the light switching instruction includes turning on the high beam or turning off the high beam; according to the light switching instruction, the vehicle light is controlled. The present invention comprehensively analyzes the internal data and external environment data of the vehicle, and the data can be obtained without the vehicle being additionally loaded with a camera. The pre-trained decision model is used to automatically judge and switch the high and low beams, thereby effectively improving the safety and comfort of night driving. The method not only reduces the operating burden of the driver, but also avoids the interference of the misuse of the high beam to other road users through intelligent decision-making, improves the safety of the overall road environment, and can solve the technical problem in the related technology that, for economical models, if there is no camera, the adaptive high and low beam lighting system cannot be equipped.

[0039] Optionally, the vehicle internal data includes at least one of the following: vehicle speed, high-precision map traffic flow, and high-precision map road conditions.

[0040] It is understandable that by analyzing these internal data, the system can more accurately judge the vehicle's environment and driving status. For example, on rural roads with low speed and low traffic volume, the system is more inclined to turn on the high beam to increase the lighting range; while in cities or highways with high speed and high traffic volume, it is more likely to turn off the high beam to avoid interfering with other vehicles, thereby improving driving safety and efficiency.

[0041] Optionally, the external environment data includes at least one of the following: light intensity, GPS time, and weather.

[0042] It is understandable that the introduction of external environmental data enables the system to make more accurate lighting switching decisions based on actual external environmental conditions (such as weather conditions, time, etc.). For example, under conditions of low light intensity and bad weather, the system will prioritize turning on the high beam to ensure the driver's clear vision. At night when the light intensity is moderate and the weather is good, the low beam will be selected to reduce the impact on other road users, thereby enhancing the driving experience and road safety.

[0043] Optionally, the high-beam and low-beam switching decision model is constructed based on a back-propagation neural network algorithm.

[0044] Specifically, the decision-making model constructed using the neural network algorithm can automatically identify and analyze the key factors affecting the switching of high and low beam headlights by learning a large amount of historical data, thereby realizing a more intelligent and flexible lighting control strategy, and effectively improving the system's adaptability and decision-making accuracy in complex environments.

[0045] Optionally, determining a light switching instruction based on the decision result includes: in response to the decision result indicating that the high beam needs to be turned on, determining the light switching instruction is to turn on the high beam; or, in response to the decision result indicating that the high beam needs to be turned off, determining the light switching instruction is to turn off the high beam.

[0046] Specifically, this instruction generation mechanism based on decision results ensures the timeliness and accuracy of light switching, avoiding safety hazards caused by decision delays or errors. Especially when driving at night, it can quickly respond to environmental changes and provide the driver with the best lighting solution.

[0047] Optionally, the training of the high and low beam switching decision model includes: obtaining training data, wherein the training data is collected by a preset collection vehicle, and the training data includes historical vehicle internal data and historical external environment data; based on the training data, training a pre-built model to be trained to obtain the high and low beam switching decision model.

[0048] Specifically, by using the actual collected vehicle data for model training, the decision-making model can be made closer to the real driving environment, which improves the practicality and reliability of the model and ensures the intelligent light switching effect under various driving conditions.

[0049] Exemplarily, an embodiment of an execution system is provided as follows:

[0050] The system includes data acquisition module, logic judgment module and execution module. The main functions of each module are as follows:

[0051] The data module is used to collect the following data:

[0052] 1) Vehicle speed: Judging the current vehicle speed can indirectly determine the current road traffic volume, which is used as one of the main bases for judging the switching of high and low beam headlights.

[0053] 2) Light intensity: It can indirectly reflect the current field of vision and is used as one of the main bases for judging whether to switch between high and low beams.

[0054] 3) GPS time: Big data is used to determine whether it is sunset, a holiday, or whether it is rush hour in the morning or evening. This is used as one of the main bases for determining the switching between high and low beams.

[0055] 4) High-precision map traffic flow, road conditions, and surrounding environment conditions: Collect traffic flow information on the current road section.

[0056] 5) Weather: Rainy and foggy days may affect the driver's vision, which is also one of the main criteria for judging whether to turn on the high beam. Here, it is used as one of the main criteria for judging the switching between high and low beam.

[0057] The logic judgment module is used for:

[0058] By collecting data from drivers' daily driving to form a training data set, and training it through the BP neural network algorithm, we can get an intelligent IHC high and low beam switching decision model. In the decision model establishment stage: we need to use the actual operation data of seed users as the basic training set, input it into the BP neural network, and then learn it to get a decision model based on the BP neural network.

[0059] During the model application phase, data on relevant influencing factors inside the vehicle are collected through the vehicle, and external data is crawled and input into the decision-making model. The decision results are given through calculation and analysis, thus completing the application of the model.

[0060] Finally, in the application, internal data such as vehicle speed is collected through the vehicle side, and relevant external data is obtained through the TSP background, which is input into the trained model to give the final decision result, that is, whether to turn on the high beam.

[0061] The execution module is used to:

[0062] The vehicle side executes according to the decision result and turns the high beam on / off.

[0063] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a grid device, etc.) to execute the methods of various embodiments of the present invention.

[0064] In this embodiment, a vehicle high and low beam switching system is also provided, and the device is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0065] Figure 2 FIG. 2 is a structural block diagram of a vehicle high and low beam light switching system 200 according to one embodiment of the present invention. Figure 2As shown, the system includes: an acquisition module 201, used to acquire vehicle operation data, wherein the vehicle operation data includes vehicle internal data and external environment data; a first determination module 202, used to determine a decision result based on the vehicle operation data and a high-beam and low-beam switching decision model, wherein the high-beam and low-beam switching decision model includes a correspondence between the vehicle operation data and the decision result; a second determination module 203, used to determine a light switching instruction based on the decision result, wherein the light switching instruction includes turning on the high beam or turning off the high beam; a control module 204, used to control the vehicle light according to the light switching instruction.

[0066] Optionally, the vehicle internal data acquired by the acquisition module 201 includes at least one of the following: vehicle speed, high-precision map traffic flow, and high-precision map road conditions.

[0067] Optionally, the external environment data acquired by the acquisition module 201 includes at least one of the following: light intensity, GPS time, and weather.

[0068] Optionally, the high-beam and low-beam switching decision model in the first determination module 202 is constructed based on a back-propagation neural network algorithm.

[0069] Optionally, the second determination module 203 is further used to: in response to the decision result indicating that the high beam needs to be turned on, determine the light switching instruction to turn on the high beam; or, in response to the decision result indicating that the high beam needs to be turned off, determine the light switching instruction to turn off the high beam.

[0070] Optionally, the training of the high and low beam switching decision model in the first determination module 202 includes: obtaining training data, wherein the training data is collected by a preset collection vehicle, and the training data includes historical vehicle internal data and historical external environment data; according to the training data, training the pre-built model to be trained to obtain the high and low beam switching decision model.

[0071] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the above-mentioned vehicle high and low beam switching method.

[0072] Optionally, in this embodiment, the processor in the above electronic device may be configured to run a computer program to perform the following steps:

[0073] Step S101, obtaining vehicle operation data, wherein the vehicle operation data includes vehicle internal data and external environment data.

[0074] Step S102, determining a decision result based on the vehicle operation data and using a pre-trained high-beam and low-beam switching decision model, wherein the high-beam and low-beam switching decision model includes a correspondence between the vehicle operation data and the decision result.

[0075] Step S103: determining a light switching instruction according to the decision result, wherein the light switching instruction includes turning on the high beam or turning off the high beam.

[0076] Step S104, controlling the vehicle lights according to the light switching instruction.

[0077] Optionally, the vehicle internal data includes at least one of the following: vehicle speed, high-precision map traffic flow, and high-precision map road conditions.

[0078] Optionally, the external environment data includes at least one of the following: light intensity, GPS time, and weather.

[0079] Optionally, the high-beam and low-beam switching decision model is constructed based on a back-propagation neural network algorithm.

[0080] Optionally, determining a light switching instruction based on the decision result includes: in response to the decision result indicating that the high beam needs to be turned on, determining the light switching instruction is to turn on the high beam; or, in response to the decision result indicating that the high beam needs to be turned off, determining the light switching instruction is to turn off the high beam.

[0081] Optionally, the training of the high and low beam switching decision model includes: obtaining training data, wherein the training data is collected by a preset collection vehicle, and the training data includes historical vehicle internal data and historical external environment data; based on the training data, training a pre-built model to be trained to obtain the high and low beam switching decision model.

[0082] An embodiment of the present invention further provides a non-volatile storage medium, in which a computer program is stored, wherein the computer program is configured to execute the vehicle high-beam and low-beam switching method in any of the above embodiments when running.

[0083] Optionally, in this embodiment, the above-mentioned computer program may be configured to store a computer program for performing the following steps:

[0084] Step S101, obtaining vehicle operation data, wherein the vehicle operation data includes vehicle internal data and external environment data.

[0085] Step S102, determining a decision result based on the vehicle operation data and using a pre-trained high-beam and low-beam switching decision model, wherein the high-beam and low-beam switching decision model includes a correspondence between the vehicle operation data and the decision result.

[0086] Step S103: determining a light switching instruction according to the decision result, wherein the light switching instruction includes turning on the high beam or turning off the high beam.

[0087] Step S104, controlling the vehicle lights according to the light switching instruction.

[0088] Optionally, the vehicle internal data includes at least one of the following: vehicle speed, high-precision map traffic flow, and high-precision map road conditions.

[0089] Optionally, the external environment data includes at least one of the following: light intensity, GPS time, and weather.

[0090] Optionally, the high-beam and low-beam switching decision model is constructed based on a back-propagation neural network algorithm.

[0091] Optionally, determining a light switching instruction based on the decision result includes: in response to the decision result indicating that the high beam needs to be turned on, determining the light switching instruction is to turn on the high beam; or, in response to the decision result indicating that the high beam needs to be turned off, determining the light switching instruction is to turn off the high beam.

[0092] Optionally, the training of the high and low beam switching decision model includes: obtaining training data, wherein the training data is collected by a preset collection vehicle, and the training data includes historical vehicle internal data and historical external environment data; based on the training data, training a pre-built model to be trained to obtain the high and low beam switching decision model.

[0093] An embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the vehicle high-beam and low-beam switching method described in any of the above embodiments.

[0094] Optionally, in this embodiment, the above computer program implements the following steps when executed by a processor:

[0095] Step S101, obtaining vehicle operation data, wherein the vehicle operation data includes vehicle internal data and external environment data.

[0096] Step S102, determining a decision result based on the vehicle operation data and using a pre-trained high-beam and low-beam switching decision model, wherein the high-beam and low-beam switching decision model includes a correspondence between the vehicle operation data and the decision result.

[0097] Step S103: determining a light switching instruction according to the decision result, wherein the light switching instruction includes turning on the high beam or turning off the high beam.

[0098] Step S104, controlling the vehicle lights according to the light switching instruction.

[0099] Optionally, the vehicle internal data includes at least one of the following: vehicle speed, high-precision map traffic flow, and high-precision map road conditions.

[0100] Optionally, the external environment data includes at least one of the following: light intensity, GPS time, and weather.

[0101] Optionally, the high-beam and low-beam switching decision model is constructed based on a back-propagation neural network algorithm.

[0102] Optionally, determining a light switching instruction based on the decision result includes: in response to the decision result indicating that the high beam needs to be turned on, determining the light switching instruction is to turn on the high beam; or, in response to the decision result indicating that the high beam needs to be turned off, determining the light switching instruction is to turn off the high beam.

[0103] Optionally, the training of the high and low beam switching decision model includes: obtaining training data, wherein the training data is collected by a preset collection vehicle, and the training data includes historical vehicle internal data and historical external environment data; based on the training data, training a pre-built model to be trained to obtain the high and low beam switching decision model.

[0104] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0105] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0106] In some embodiments provided in the present application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0107] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed over multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0108] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0109] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server or a grid device, etc.) to perform all or part of the steps of the methods of various embodiments of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, etc., which can store program codes.

[0110] The above are only preferred embodiments of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for switching between high and low beam lights of a vehicle, characterized in that: The method comprises: Acquiring vehicle operation data, wherein the vehicle operation data includes vehicle internal data and external environment data; Determine a decision result according to the vehicle operation data using a pre-trained high-beam and low-beam switching decision model, wherein the high-beam and low-beam switching decision model includes a correspondence between the vehicle operation data and the decision result; Determining a light switching instruction according to the decision result, wherein the light switching instruction includes turning on the high beam or turning off the high beam; The vehicle lights are controlled according to the light switching instruction.

2. The vehicle high and low beam light switching method according to claim 1, characterized in that: The vehicle internal data includes at least one of the following: vehicle speed, high-precision map traffic flow and high-precision map road conditions.

3. The vehicle high and low beam light switching method according to claim 1, characterized in that: The external environment data includes at least one of the following: light intensity, GPS time, and weather.

4. The vehicle high and low beam light switching method according to claim 1, characterized in that: The high-beam and low-beam switching decision model is constructed based on a back-propagation neural network algorithm.

5. The vehicle high and low beam switching method according to claim 1, characterized in that: Determining the light switching instruction according to the decision result includes: In response to the decision result indicating that the high beam lights need to be turned on, determining that the light switching instruction is to turn on the high beam lights; or, In response to the decision result indicating that the high beam needs to be turned off, the light switching instruction is determined to be turning off the high beam.

6. The vehicle high and low beam light switching method according to claim 1, characterized in that: The training of the high and low beam switching decision model includes: Acquiring training data, wherein the training data is collected by a preset collection vehicle, and the training data includes historical vehicle internal data and historical external environment data; The pre-built model to be trained is trained according to the training data to obtain the high and low beam switching decision model.

7. A vehicle high and low beam switching system, characterized in that: include: An acquisition module, used to acquire vehicle operation data, wherein the vehicle operation data includes vehicle internal data and external environment data; A first determination module is used to determine a decision result based on the vehicle operation data and using a high-beam and low-beam switching decision model, wherein the high-beam and low-beam switching decision model includes a correspondence between the vehicle operation data and the decision result; A second determination module, configured to determine a light switching instruction according to the decision result, wherein the light switching instruction includes turning on the high beam or turning off the high beam; The control module is used to control the vehicle lights according to the light switching instruction.

8. An electronic device comprising a memory and a processor, characterized in that: The memory stores a computer program, and the processor is configured to run the computer program to execute the vehicle high-beam and low-beam switching method described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program is configured to execute the vehicle high-beam and low-beam switching method described in any one of claims 1 to 6 when running on a computer or a processor.

10. A computer program product, characterized in that It comprises a computer program, which, when executed by a processor, implements the vehicle high-beam and low-beam switching method according to any one of claims 1 to 6.

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