Vehicle lighting control methods, devices, and equipment

Abstract

This application discloses a vehicle headlight control method, device, and equipment, relating to the field of automotive technology. The method includes the following steps: real-time acquisition of environmental information, which describes the environment of the road where a first vehicle is located; prediction of road condition information based on the environmental information, which indicates the road conditions and lighting conditions of a first road segment not yet traversed on the driving path; analysis of the road condition information to obtain headlight scene demand information within the first road segment, which describes the headlight control needs of the first vehicle within the first road segment; generation of headlight control commands based on the headlight scene demand information and environmental information; and control of the headlights of the first vehicle based on the headlight control commands. This method enables decisions on headlight control methods to be made solely at the vehicle end, eliminating the need to transmit information from the vehicle to the cloud for decision-making, thus improving decision-making efficiency and data security.

Description

Technical Field

[0001] This application relates to the field of automotive technology, and in particular to a method, device, and equipment for controlling vehicle lights. Background Technology

[0002] With the development of autonomous driving technology, headlight control has become an important function. Automatically turning on headlights in poor lighting conditions and low visibility can greatly improve driving safety and convenience.

[0003] In related technologies, vehicle lighting control primarily relies on information collection and cloud-based decision-making processes. The vehicle uses sensors and other devices to collect environmental information and transmits it to the cloud. The cloud then analyzes and makes decisions based on this environmental information to generate control commands, which are transmitted back to the vehicle, thereby achieving precise control of the vehicle lights.

[0004] However, there is a risk of information leakage during the transmission of information between the vehicle and the cloud, and the time-consuming data transmission can also affect decision-making efficiency, thereby increasing driving risks. Summary of the Invention

[0005] This application provides a vehicle lighting control method, apparatus, and device that enables decisions on the vehicle lighting control method to be made solely at the vehicle end, eliminating the need to transmit information from the vehicle to the cloud for decision-making, thus improving decision-making efficiency and data security. The technical solution is as follows:

[0006] On the one hand, a vehicle lighting control method is provided, the method comprising:

[0007] Real-time collection of environmental information, which is used to describe the environment of the road where the first vehicle is located;

[0008] Based on the environmental information, road condition information is predicted, and the road condition information is used to indicate the road conditions and lighting conditions of the first road segment that has not been traveled on the driving path.

[0009] Analyzing the road condition information, the vehicle lighting scene requirement information within the first road segment is obtained. The vehicle lighting scene requirement information is used to describe the vehicle lighting control requirements of the first vehicle within the first road segment.

[0010] Based on the vehicle lighting scenario requirements information and the environmental information, a vehicle lighting control command is generated, and the vehicle lighting of the first vehicle is controlled based on the vehicle lighting control command.

[0011] On the other hand, a vehicle lighting control device is provided, the device comprising:

[0012] The data acquisition module is used to collect environmental information in real time, and the environmental information is used to describe the environment of the road where the first vehicle is located.

[0013] The prediction module is used to predict road condition information based on the environmental information, wherein the road condition information is used to indicate the road conditions and lighting conditions of the first road segment not yet traveled on the driving path.

[0014] The analysis module is used to analyze the road condition information to obtain the vehicle lighting scene requirement information in the first road segment. The vehicle lighting scene requirement information is used to describe the vehicle lighting control requirements of the first vehicle in the first road segment.

[0015] The control module is used to generate vehicle lighting control commands based on the vehicle lighting scene requirement information and the environmental information, and to control the vehicle lights of the first vehicle based on the vehicle lighting control commands.

[0016] In an optional embodiment, the illumination conditions include illumination values ​​at multiple locations along the first road segment;

[0017] The analysis module is configured to, in response to the illumination conditions where the illumination value corresponding to a first location point is lower than a preset first illumination threshold, determine that the vehicle lighting scene requirement information includes turning on the vehicle lights or keeping the vehicle lights on at the first location point; or, in response to the illumination conditions where the illumination value corresponding to a second location point is higher than a preset second illumination threshold, determine that the vehicle lighting scene requirement information includes turning off the vehicle lights or keeping the vehicle lights off at the second location point; or, in response to the illumination conditions where the illumination value corresponding to a third location point is higher than the first illumination threshold and lower than the second illumination threshold, determine that the vehicle lighting scene requirement information includes keeping the vehicle lights unchanged at the third location point.

[0018] In an optional embodiment, the vehicle headlight scene requirement information further includes vehicle headlight brightness information, which is used to indicate the headlight brightness when the headlights of the first vehicle are turned on.

[0019] The analysis module is further configured to calculate a first difference between the illumination value corresponding to the first location point and the first illumination threshold; and to obtain the corresponding headlight brightness information based on the first difference.

[0020] In an optional embodiment, the traffic information includes lighting conditions, weather type, and road type corresponding to multiple location points in the first road segment;

[0021] The analysis module is further configured to obtain a preset vehicle headlight scene mapping table, which includes a first mapping relationship between various lighting conditions and various vehicle headlight control scenarios, a second mapping relationship between various weather types and the various vehicle headlight control scenarios, and a third mapping relationship between various road types ahead and the various vehicle headlight control scenarios. Based on the road condition information, the module determines the target vehicle headlight control scenarios corresponding to the multiple location points from the vehicle headlight scene mapping table to obtain vehicle headlight scene demand information within the first road segment.

[0022] In an optional embodiment, the control module is further configured to update the environmental information in real time to obtain the environmental information at the current moment, wherein the environmental information at the current moment includes the vehicle position of the first vehicle; wherein the vehicle lighting scene requirement information includes at least one location point in the first road segment where there is a vehicle lighting control requirement; and if the at least one location point and the vehicle position of the first vehicle meet a preset instruction generation condition, the vehicle lighting control instruction is generated based on the vehicle lighting scene requirement information.

[0023] In an optional embodiment, the control module is further configured to calculate, for the i-th location among the at least one location points, the i-th distance between the vehicle position of the first vehicle and the i-th location point; i is a positive integer; in response to the i-th distance being less than or equal to a preset distance threshold, determine that the i-th location point and the vehicle position of the first vehicle meet a preset instruction generation condition; wherein, the vehicle lighting scene requirement information includes a control type for controlling the vehicle lights at the at least one location point, the control type including turning off the vehicle lights, turning on the vehicle lights, and keeping the vehicle lights unchanged; and based on the control type for controlling the vehicle lights at the i-th location point in the vehicle lighting scene requirement information, generate a vehicle lighting control instruction corresponding to the i-th location point.

[0024] In an optional embodiment, the traffic information includes the road type of the first road segment and the illumination values ​​of multiple locations within the first road segment;

[0025] The analysis module is further configured to obtain a preset coefficient table, which includes safety coefficients corresponding to various road types. The values ​​of the safety coefficients describe the safety level of vehicles traveling on different types of roads, and the values ​​of the safety coefficients are positively correlated with the safety level. Based on the road type of the first road segment, a first coefficient is obtained from the coefficient table. The first coefficient indicates the safety level of the first vehicle traveling on the first road segment. Multiple values ​​corresponding to the multiple location points are obtained by multiplying the illumination values ​​of the multiple location points with the first coefficient. Based on the multiple values, the vehicle lighting scene corresponding to the multiple location points is determined to obtain the vehicle lighting scene requirement information.

[0026] In an optional embodiment, the analysis module is further configured to, in response to the presence of at least one value among the plurality of values ​​that meets a preset headlight turning-on requirement, determine at least one location point corresponding to each of the at least one value, and determine that the headlight scene requirement information includes turning on the headlights or keeping the headlights on at the at least one location point.

[0027] On the other hand, a computer device is provided, the computer device including a processor and a memory, the memory storing at least one instruction, at least one program, code set or instruction set, the at least one instruction, the at least one program, the code set or instruction set being loaded and executed by the processor to implement the vehicle light control method as described in any of the embodiments of this application above.

[0028] On the other hand, a computer-readable storage medium is provided, wherein at least one instruction, at least one program, code set, or instruction set is stored therein, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement the vehicle light control method as described in any of the embodiments of this application above.

[0029] On the other hand, a computer program product or computer program is provided, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform any of the vehicle lighting control methods described in the above embodiments.

[0030] The beneficial effects of the technical solutions provided in this application include at least the following:

[0031] By collecting environmental information, the system can predict road conditions and lighting conditions in the section of road the first vehicle will pass through, determining whether there are scenarios requiring the headlights to be turned on or off. This provides sufficient time to activate or deactivate the headlights in advance, enabling automatic headlight control. The entire process of information collection and headlight control command generation is executed on the vehicle itself. There is no need to send the collected information to the cloud for analysis and decision-making, or for the cloud to return the decision results to the vehicle for headlight control. This reduces the risk of information leakage during transmission, improves data security, reduces transmission time for information and decision results, and increases decision-making and headlight control efficiency. In extreme situations, the system can automatically activate or deactivate the headlights in a timely manner when road conditions or lighting conditions change, ensuring driver safety. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in the embodiments of this application, 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 this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 This is a schematic diagram of a vehicle lighting control system provided in an exemplary embodiment of this application;

[0034] Figure 2 This is an exemplary embodiment of the present application based on Figure 1 A schematic diagram illustrating the interaction method of the vehicle lighting control system;

[0035] Figure 3 This is a flowchart of a vehicle headlight control method provided in an exemplary embodiment of this application;

[0036] Figure 4 This is a structural block diagram of a vehicle lighting control device provided in an exemplary embodiment of this application;

[0037] Figure 5 This is a structural block diagram of a computer device provided in an exemplary embodiment of this application. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0039] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0040] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0041] It should be noted that all information and data involved in this application are authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0042] It should be understood that although the terms first, second, etc., may be used in this application to describe various information, this information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, a first parameter may also be referred to as a second parameter, and similarly, a second parameter may also be referred to as a first parameter. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0043] First, a brief introduction to the terms used in the embodiments of this application:

[0044] The Global Positioning System (GPS) is a satellite-based radio navigation system. It consists of a space segment (satellite constellation), a ground control segment, and user equipment (such as in-vehicle GPS receivers). The satellite constellation transmits radio signals containing timestamps and satellite positions back to the Earth's surface. The in-vehicle GPS receiver receives signals from multiple satellites, calculates the distance to each satellite based on the signal propagation time (using the relationship between the speed of light and signal propagation time), and then uses triangulation to determine the vehicle's three-dimensional position on Earth (longitude, latitude, and altitude).

[0045] The main functions of GPS include positioning and navigation, speed recording, and track recording. In in-vehicle navigation systems, GPS can accurately determine the vehicle's location and display it on an electronic map. Drivers can then follow navigation prompts along preset routes. For example, after the driver enters their destination, the navigation system combines GPS positioning information and map data to provide the driver with the best route, including turn prompts and estimated arrival time.

[0046] In this application, the road condition information of the road segment to be passed in the future can be predicted based on the current location and driving trajectory of the vehicle provided by GPS.

[0047] Electronic Control Unit (ECU): The ECU is the core component of an automotive electronic control system. It receives signals from various sensors, processes the data according to pre-programmed programs and control strategies, and finally sends control commands to the actuators to achieve precise control of specific automotive systems.

[0048] Microcontroller Unit (MCU): An MCU is a microcomputer system that integrates a central processing unit, memory, timers / counters, input / output interfaces, and other functional components onto a single chip. It is primarily used to control the operation of various electronic devices. In vehicles, the MCU is mainly responsible for controlling various basic vehicle functions. For example, an MCU can control simple devices such as door locks, window operation, and interior lights.

[0049] A deep learning accelerator is a hardware device or processor architecture specifically designed to accelerate the training and inference processes of deep learning models. The training and inference of deep learning models involve numerous complex matrix operations, convolution operations, and other computationally intensive tasks, demanding significant computational resources. Deep learning accelerators, by optimizing hardware architecture and employing specialized computing units, can efficiently handle these specific types of computational tasks, thereby significantly improving the training speed and inference efficiency of deep learning models.

[0050] The deep learning accelerator in the vehicle-local processor described in this application is designed for automotive application scenarios and needs. It can process complex computational tasks locally in real time: performing inference based on collected environmental information, determining whether the first vehicle meets the conditions for turning the headlights on / off, making a decision, and generating instructions for controlling the headlights. This eliminates the need to transmit data to the cloud for processing, thereby reducing reliance on the network, improving system response speed and reliability, and enhancing the vehicle's intelligent experience and safety.

[0051] With the continuous evolution and expansion of autonomous driving technology, headlight control is playing an increasingly important role in the entire autonomous driving system. In real-world driving scenarios, when encountering complex road conditions with poor lighting and significantly reduced visibility, such as driving at night, in severe weather (such as heavy rain, dense fog, sandstorms, etc.), or driving in dimly lit tunnels, underground parking lots, and other special road sections, if the vehicle can automatically and promptly turn on its headlights, it will improve driving safety and reduce distractions that may occur due to manual operation of the headlights.

[0052] In related technologies, the realization of vehicle lighting control relies on a system of vehicle-side information collection and cloud-based decision-making. The vehicle is equipped with various types of sensors that collect environmental information such as light intensity, object shapes, and road conditions. After collection, the vehicle transmits the environmental information data to a cloud server. The cloud server then makes decisions based on the received environmental information, generating commands for vehicle lighting control. The cloud then transmits these control commands back to the vehicle via a communication network. Upon receiving the control commands, the vehicle precisely controls the vehicle lights accordingly.

[0053] However, information is transmitted between the vehicle and the cloud via wireless communication networks. These networks are vulnerable to attack in open environments, posing a risk of information leakage during transmission. Furthermore, information transmission between the vehicle and the cloud takes time. In autonomous driving scenarios with high real-time requirements, even brief data transmission delays can lead to lags in cloud decision-making. For example, when a vehicle suddenly enters an area with rapidly changing lighting (such as suddenly entering a dark tunnel from bright sunlight), the cloud may be unable to quickly make decisions and issue control commands due to the time-consuming data transmission. This can cause a delay in turning on the headlights, creating a blind spot for the driver for a short period and significantly increasing driving risks.

[0054] This application provides a vehicle lighting control method that can realize information collection, decision analysis, instruction generation and vehicle lighting control based on the vehicle's local computing power. It eliminates the need to send the collected environmental information to the cloud and then make decisions and issue instructions from the cloud, thereby reducing the risk of leakage and decision delay during information transmission and improving decision efficiency.

[0055] Secondly, the vehicle lighting control system involved in the embodiments of this application will be described in an illustrative manner. Please refer to [the relevant documentation / reference]. Figure 1 The vehicle lighting control system 100 is a system inside the first vehicle.

[0056] The vehicle lighting control system 100 includes vehicle hardware and software 101, local processor 102, ECU 103, and vehicle lights 104.

[0057] The onboard hardware and software 101 collects environmental information, including road conditions and lighting conditions of the road where the first vehicle is located. The local processor 102 analyzes and processes the environmental information collected by the onboard hardware and software 101 and outputs a decision result, which is used to indicate how to control the headlights 104. The ECU 103 converts the decision output by the local processor 102 into control commands and controls the headlights 104 to perform corresponding operations, such as turning the headlights 104 on / off at the time specified by the control command.

[0058] Indicative, Figure 2 Based on Figure 1 The diagram shows the interaction method of the vehicle lighting control system.

[0059] The vehicle-mounted hardware and software 101 includes, but is not limited to, a camera 1011, a radar 1012, and a vehicle navigation system 1013. The camera 1011 and radar 1012 can collect lighting conditions and road condition information of the first vehicle's current driving state. The lighting conditions are used to describe the visibility and visibility of the current environment. The vehicle navigation system 1013 can obtain the road conditions ahead. Based on the information collected by the vehicle-mounted hardware and software 101, the road conditions of the road the first vehicle is about to enter can be predicted, and it can be promptly determined whether there are sections of road with poor visibility ahead.

[0060] The local processor 102 includes a high-performance MCU 1021 and a deep learning accelerator 1022. First, the MCU 1021 classifies and judges the environmental information. The road condition information of the road ahead obtained by the vehicle navigation 1013 is processed by the MCU 1021 to determine the possible scenarios in the road ahead that require the headlights 104 to be turned on, including but not limited to the first vehicle entering a tunnel, rainy days, foggy days, etc.

[0061] The system then collects current road condition information via camera 1011, radar 1012, and in-vehicle navigation 1013. This road condition information, along with information about the headlights being on, is fed into a deep learning accelerator 1022. The deep learning accelerator 1022 uses deep learning algorithms to determine whether the current road conditions meet the headlight change conditions. Meeting the headlight change conditions means that the first vehicle is about to reach the location where the headlights 104 need to be turned on. When the headlight change conditions are met, the decision information is transmitted to the ECU 103, which then controls the headlights 104 accordingly, for example, by turning them on. The decision information specifies the control method for the headlights 104, including but not limited to the on / off state of the headlights 104, the brightness of the headlights 104 when on, and the duration of the on / off state.

[0062] The vehicle lighting control system 100 is a system inside the first vehicle. The above-mentioned information collection, information processing and decision control processes are all completed at the vehicle end, without the need for processing on the server / cloud, which improves decision-making efficiency and data security.

[0063] Based on the above-described terminology and application scenarios, the vehicle lighting control method provided in this application will be explained, taking the method executed by the on-board terminal of the first vehicle as an example. Figure 3 As shown, Figure 3 This is a flowchart of a vehicle headlight control method provided in an exemplary embodiment of this application. The method includes the following steps.

[0064] Step 310: Collect environmental information in real time.

[0065] Among them, environmental information is used to describe the environment of the road where the first vehicle is located.

[0066] Optionally, the first vehicle is equipped with cameras, various sensors, and an in-vehicle navigation system to collect environmental information during its operation. This environmental information includes, but is not limited to, the following:

[0067] (1) Weather conditions: rain / snow / fog / sunny / cloudy / overcast weather, etc. Weather condition information can be collected by meteorological sensors installed on the first vehicle, or by receiving data from external meteorological services via wireless network.

[0068] (2) Road conditions: Road smoothness (e.g., whether there are potholes or bumps), road type (highway / city road / country road / mountain road), and road slipperiness (dry / wet / waterlogged, etc.). Road condition information can be collected through vehicle chassis sensors and wheel speed sensors, or by obtaining map data (e.g., high-precision maps provide road type information) via wireless networks.

[0069] (3) Traffic sign and facility information: The location and status of various traffic signs (such as speed limit signs, no overtaking signs, etc.) and traffic facilities (such as traffic lights, toll booths, etc.) on the road. This information can be collected by the camera of the first vehicle.

[0070] (4) Surrounding vehicle information: The number, location, speed, and direction of travel of other vehicles around the first vehicle. Surrounding vehicle information can be collected by sensors such as millimeter-wave radar, lidar, or cameras installed on the vehicle.

[0071] (5) Information on buildings and obstacles around the road: the location and height of buildings, and obstacles on both sides of the road (such as temporary construction barriers, parked vehicles, etc.). LiDAR can perform three-dimensional scanning of the environment around the vehicle to obtain detailed information on these buildings and obstacles.

[0072] Among them, weather conditions, traffic signs and traffic facility information, surrounding vehicle information, roadside building and obstacle information are related to the visibility / light conditions of the road where the first vehicle is currently located.

[0073] For example, rain, snow, or dense fog can reduce the intensity and clarity of light, interfering with drivers' vision and reducing lighting conditions and visibility. Sunny days offer ample sunlight and generally high visibility. On cloudy days, clouds block some sunlight, reducing light intensity.

[0074] For example, when surrounding vehicles turn on their headlights, the strong light improves the lighting conditions of the surrounding environment. However, when the strong light shines directly into the eyes of the driver of the first vehicle, it will affect the driver's vision and reduce visibility.

[0075] For example, the obstruction of light by tall buildings around the road can affect lighting conditions. When the sun is low in the sky, the shadows of tall buildings may cover parts of the road, reducing the light intensity on those parts and resulting in poor lighting conditions.

[0076] For example, some traffic facilities, such as streetlights and traffic lights, are directly related to lighting conditions. The brightness and distribution of streetlights affect the lighting conditions on roads at night.

[0077] Step 320: Predict road condition information based on environmental information.

[0078] Among them, the road condition information is used to indicate the road conditions and lighting conditions of the first road segment that has not been traveled on the driving route.

[0079] Optionally, based on the current road conditions described by the environmental information, the road conditions of the first road segment that the vehicle is about to pass through are extended and inferred to obtain road condition information.

[0080] For example, weather conditions in environmental information can reflect lighting conditions in one aspect. For instance, if the current weather is sunny or cloudy, and there are no signs of weather change (such as weather sensors not detecting distant rain clouds or fog, and meteorological data received by the wireless network showing stable weather in the short term), it can be inferred that lighting conditions are good for a distance ahead. Conversely, if the current weather is snowy, rainy, or foggy, the current low light and poor visibility may continue into the first section of the road ahead.

[0081] For example, road conditions in environmental information can reflect the road surface type of the first road segment and infer the lighting conditions ahead. For instance, if road conditions indicate that the vehicle is currently traveling on a highway with open visibility and generally good lighting, it is inferred that the first road segment is still a highway and the lighting conditions remain unchanged. Alternatively, if road conditions indicate that the vehicle is currently traveling on a rural road with trees or mountains blocking the light, the lighting conditions may be poor. For mountain roads, combined with map information, if the first vehicle is traveling along the sunny side of a mountain and is about to turn onto the shady side, the lighting conditions may worsen.

[0082] For example, traffic signs and infrastructure information in the environmental information can indicate whether there is construction or a special type of road ahead. For instance, signs indicating "tunnel ahead" or "underpass ahead" suggest that the lighting in the first section of the road will be significantly reduced, requiring the headlights to be turned on.

[0083] For example, the surrounding vehicle information in the environmental information can reflect the movement trends of surrounding vehicles, and infer lighting conditions based on the on / off status of the vehicle lights and the vehicle's driving status. For instance, surrounding vehicles include vehicles driving in front of the first vehicle that have their lights on and are driving at a relatively slow speed. Even if there are no obvious signs of change in the current lighting conditions and weather conditions, this indicates that there may be poorly lit areas ahead, such as when entering a tunnel or at the entrance of a dimly lit underground parking lot.

[0084] For example, the building and obstacle information in the environmental information can determine whether the height of the buildings will create shadows, affecting the lighting conditions of the first road segment. For instance, if there is a tall building on one side of the road ahead of the first vehicle, and the vehicle is driving into the building's shadow area, and the map shows a similar distribution of buildings over a distance ahead, it can be inferred that the first road segment will remain in shadow, with reduced light and poor lighting conditions. Through this method, the current environment of the first vehicle can be extended and predicted, forecasting the road conditions and lighting conditions of the first road segment it will soon enter, thus obtaining the road condition information for the first segment.

[0085] In some embodiments, the first location of the first vehicle at the current moment can be determined based on environmental information, a map corresponding to the road segment where the first location is located can be obtained from the vehicle navigation, and road segment information can be obtained from the map based on the first vehicle's driving path as road condition information for predicting the first road segment that the first vehicle will soon pass through.

[0086] For example, the environmental information includes the positioning information collected by the vehicle navigation system of the first vehicle. The vehicle navigation system relies on a GPS receiver to receive satellite signals and obtain information such as the longitude, latitude, and altitude of the first vehicle to obtain the positioning information.

[0087] A high-precision map is acquired, and the vehicle's trajectory is compared with the map's road layout and shape to determine its initial location. The map records road types; by extending the road network according to the direction of travel, subsequent road segments are predicted, identifying the first road segment the vehicle is about to enter. Traffic signs and surrounding landmarks captured by the vehicle's camera add detail and correct the direction for the road segment prediction. After determining the first road segment, its road conditions are obtained from the map.

[0088] In some embodiments, a light sensor is deployed on the first vehicle. This sensor converts light intensity into an electrical signal, and the light intensity is determined based on the magnitude of the electrical signal. Once a sharp drop in light intensity is detected, the information collected by the camera is analyzed to identify entrance features of special areas such as tunnels, thus determining whether the first vehicle has entered a specially lit section of road, such as a tunnel or a tree-lined road. The lighting conditions of the first road segment to be entered are determined based on weather information and light intensity from the environmental data.

[0089] Step 330: Analyze road condition information to obtain the vehicle lighting scene requirement information in the first road segment.

[0090] Among them, the vehicle lighting scenario requirement information is used to describe the vehicle lighting control requirements of the first vehicle in the first road segment.

[0091] The road condition information for the first road segment is predicted before the first vehicle enters the first road segment, used to determine the type of headlight control when the first vehicle enters the first road segment (entrance / starting point). During the first vehicle's journey, environmental information is collected in real time, and the road condition information for the road segment within a preset distance ahead is predicted based on the currently collected environmental information. Further analysis yields the headlight scene requirements for that road segment. While the first vehicle is traveling within the first road segment, real-time environmental information collection continues. When the length of the first road segment reaches a first preset length, the first road segment is divided into multiple sub-segments by multiple location points. When the first vehicle is traveling in one of these sub-segments, the environmental information for the current sub-segment is collected, and the road condition information for the next sub-segment is predicted. Analysis then yields the headlight scene requirements for the next sub-segment.

[0092] Alternatively, in some embodiments, each sub-segment can still be regarded as an independent segment. When the length of the first segment does not reach the first preset length, the first vehicle collects the environmental information of the first segment in real time while driving on the first segment, predicts the road condition information of the adjacent forward segment (second segment), and analyzes the road condition information of the second segment to obtain the vehicle lighting scene requirement information in the second segment.

[0093] In other words, when the first vehicle is driving, environmental information is collected in real time and road conditions ahead are predicted based on the environmental information. Based on the road conditions, the headlight scene requirements ahead are predicted. Before the first vehicle enters the road ahead, the headlights are controlled in advance based on the headlight scene requirements, so that the headlight status when the first vehicle enters the road ahead meets the safety requirements.

[0094] Optionally, the vehicle headlight scenario requirement information includes the following scenarios and corresponding headlight control information: 1. When the first vehicle is traveling in the first road segment, if the visibility / lighting conditions of the surrounding environment meet the headlight turning-on requirements, the headlight control information indicates that the headlights should be turned on; 2. When the first vehicle is traveling in the first road segment, if the visibility / lighting conditions of the surrounding environment meet the headlight turning-off requirements, the headlight control information indicates that the headlights should be turned off; 3. When the first vehicle is traveling in the first road segment, if the visibility / lighting conditions of the surrounding environment do not meet either the headlight turning-on or headlight turning-off requirements, the headlight status should remain unchanged; for example, if the headlights of the first vehicle were originally in the on state, they should remain in the on state; if the headlights of the first vehicle were originally in the off state, they should remain in the off state.

[0095] Optionally, the lighting conditions include lighting values ​​at multiple locations in the first road segment. As the first vehicle travels on the first road segment, the lighting conditions may change at any time. The lighting values ​​are collected in real time, and the method of controlling the headlights when the first vehicle passes each location is determined based on the changes in the lighting values.

[0096] In some embodiments, the first road segment is divided based on distance to obtain multiple location points, the light intensity of each location point is collected to obtain the light value, and the existence of vehicle lighting scene demand information and the type of vehicle lighting scene demand information are determined based on the magnitude of the light value.

[0097] In some embodiments, the illumination intensity is collected in real time by the illumination sensor of the first vehicle during the vehicle's operation to obtain illumination intensity change data. The illumination intensity change data includes the process of illumination value change. When the change in illumination value within a unit distance / unit time reaches a preset requirement, a vehicle lighting control request is determined at that location. The type of vehicle lighting scene request information is determined based on the magnitude and direction (increase or decrease) of the illumination value change.

[0098] For example, in response to the fact that the illumination value corresponding to the first position point is lower than the preset first illumination threshold in the illumination conditions, it is determined that the vehicle lighting scene requirement information includes turning on the vehicle lights or keeping the vehicle lights on at the first position point.

[0099] For example, the unit of light intensity is lux, the first light threshold is 1000 lux, and the first location point is the tunnel entrance with poor lighting conditions. When the light sensor collects a light intensity value of 900 lux at the first location point, the light value at the first location point is lower than the first light threshold. If the headlights of the first vehicle are on, they will remain on until the first vehicle passes the first location point; if the headlights of the first vehicle are off, they will automatically turn on until the first vehicle passes the first location point.

[0100] For example, in response to the fact that the illumination value corresponding to the second location point in the illumination conditions is higher than the preset second illumination threshold, it is determined that the vehicle lighting scene requirement information includes turning off the vehicle lights or keeping the vehicle lights off at the second location point.

[0101] For example, the unit of light intensity is lux, the second light threshold is 2000 lux, and the second location point is the tunnel exit with good lighting conditions. When the light sensor collects a light intensity value of 2100 lux at the second location point, the light value at the second location point is higher than the second light threshold. If the headlights of the first vehicle are off, they will remain off until the first vehicle passes the second location point; if the headlights of the first vehicle are on, they will automatically turn off until the first vehicle passes the second location point.

[0102] For example, in response to the existence of a third location point in the lighting conditions where the lighting value is higher than the first lighting threshold and lower than the second lighting threshold, it is determined that the vehicle lighting scene requirement information includes keeping the vehicle lighting state unchanged at the third location point.

[0103] For example, the unit of illuminance is lux, the first illuminance threshold is 1000 lux, the second illuminance threshold is 2000 lux, and the third location point is an open road with clear weather and good lighting conditions. When the illuminance sensor collects a value of 1800 lux for the illuminance at the third location point, the illuminance value at the third location point is higher than the first illuminance threshold but lower than the second illuminance threshold. If the headlights of the first vehicle are off, they remain off until the first vehicle passes the third location point; if the headlights of the first vehicle are on, they remain on until the first vehicle passes the third location point. In some embodiments, the headlight scene requirement information also includes headlight brightness information, which is used to indicate the headlight brightness when the headlights of the first vehicle are on.

[0104] For the first position point in the vehicle lighting scenario requirement information that indicates the need to turn on the vehicle lights, calculate the first difference between the illumination value corresponding to the first position point and the first illumination threshold.

[0105] The corresponding headlight brightness information is obtained based on the first difference.

[0106] For example, a headlight brightness mapping table is obtained, which contains a preset mapping relationship between the range of the first difference value and the headlight brightness. The corresponding headlight brightness information is determined based on the range of the first difference value. The unit of each range is lux.

[0107] For example, when the first difference value is within the first interval [0, 250], the headlight brightness information indicates that the headlight brightness is set to 40% of the maximum headlight brightness. When the first difference value is within the second interval [250, 500], the headlight brightness information indicates that the headlight brightness is set to 60% of the maximum headlight brightness. When the first difference value is within the third interval [500, 750], the headlight brightness information indicates that the headlight brightness is set to 80% of the maximum headlight brightness. When the first difference value is within the fourth interval [750, 1000], the headlight brightness information indicates that the headlight brightness is set to 100% of the maximum headlight brightness.

[0108] In some embodiments, for the third position point in the vehicle lighting scenario requirement information that indicates the vehicle lighting status to be maintained, if the vehicle lighting status is on, a second difference between the illumination value corresponding to the third position point and the first illumination threshold, and a third difference between the illumination value corresponding to the third position point and the second illumination threshold can also be calculated. The method of adjusting the vehicle lighting brightness is determined based on the larger of the second difference and the third difference.

[0109] For example, if the second difference is greater than the third difference, the headlight brightness is reduced; if the second difference is less than the third difference, the headlight brightness is increased; if the second difference is equal to the third difference, the headlight brightness remains unchanged. The magnitude of the brightness reduction / increase is n% of the maximum headlight brightness, where n is a preset positive number.

[0110] Optionally, the traffic information includes lighting conditions, weather type, and road type for multiple locations within the first road segment.

[0111] Obtain a preset vehicle headlight scene mapping table. The vehicle headlight scene mapping table contains a first mapping relationship between various lighting conditions and various vehicle headlight control scenes, a second mapping relationship between various weather types and various vehicle headlight control scenes, and a third mapping relationship between various road types ahead and various vehicle headlight control scenes.

[0112] Based on road condition information, the target headlight control scenarios corresponding to multiple location points are determined from the headlight scene mapping table, and the headlight scene demand information in the first road segment is obtained.

[0113] For example, for the k-th location point, three mapping relationships are determined based on the vehicle light scene mapping table, corresponding to the k-th lighting condition, the k-th weather type, and the k-th road type of the k-th location point, respectively. k is a positive integer.

[0114] If at least one of the first, second, and third mapping relationships corresponding to the k-th location point indicates that the headlight control scenario is "headlights on," then the target headlight control scenario corresponding to the k-th location point is determined to be a "headlights on" scenario. If none of the first, second, and third mapping relationships corresponding to the k-th location point indicates that the headlight control scenario is "headlights off," then the target headlight control scenario corresponding to the k-th location point is determined to be a "headlights off" scenario.

[0115] Once the target headlight control scenarios corresponding to all locations are determined, the headlight scenario requirement information for the first road segment is obtained. The headlight scenario requirement information for the first road segment can indicate whether the first vehicle needs to turn its headlights on / off when driving through the first road segment, at which locations the headlights need to be turned on / off, and the duration of the headlight state each time the headlights are turned on / off.

[0116] Optionally, the traffic information includes the road type of the first road segment and the illumination values ​​of multiple locations within the first road segment.

[0117] Obtain a preset coefficient table, which includes safety coefficients for various road types. The values ​​of the safety coefficients describe the safety level of vehicles when driving on different types of roads, and the values ​​of the safety coefficients are positively correlated with the safety level.

[0118] The first coefficient is obtained from the coefficient table based on the road type of the first road segment. The first coefficient is used to indicate the safety level of the first vehicle when traveling in the first road segment.

[0119] The product of the illumination values ​​at multiple locations and the first coefficient yields multiple values ​​corresponding to each location.

[0120] Based on multiple numerical values, the corresponding vehicle lighting scenes for multiple location points are determined, and the vehicle lighting scene requirement information is obtained.

[0121] For example, in response to the fact that at least one of the multiple values ​​meets the preset headlight turning-on requirements, at least one position point corresponding to each of the at least one value is determined, and the headlight scene requirement information is determined to include turning on the headlights or keeping the headlights on at at least one position point.

[0122] For example, the preset requirement for turning on the headlights is that the product of the illumination value at a given location and the first coefficient is less than a preset product threshold.

[0123] The coefficient table includes the following: when the road type is highway / urban road, the safety factor is 10; when the road type is rural road, the safety factor is 5; when the road type is mountain road, the safety factor is 1.

[0124] The road type of the first section is an urban road, so the first coefficient is 1, the illuminance at the first location point is 1500 lux, the illuminance at the second location point is 1000 lux, and the preset product threshold is 10000.

[0125] Therefore, the value corresponding to the first position point is 1500 * 10 = 15000, which is greater than the preset product threshold of 10000. The value corresponding to the second position point is 1000 * 10 = 10000, which is equal to the preset product threshold of 10000. Thus, the first position point does not meet the preset requirements for turning on the headlights, while the second position point does meet the preset requirements for turning on the headlights.

[0126] Step 340: Generate vehicle lighting control instructions based on vehicle lighting scenario requirements and environmental information, and control the vehicle lights of the first vehicle based on the vehicle lighting control instructions.

[0127] After predicting the demand information for vehicle lights, the system updates the environmental information in real time to obtain the current environmental information, which includes the position of the first vehicle. The position of the first vehicle indicates whether it has entered the first road segment.

[0128] Among them, the vehicle lighting scenario requirement information includes at least one location point within the first road segment where there is a need for vehicle lighting control.

[0129] If the preset command generation conditions are met between at least one location point and the vehicle position of the first vehicle, a vehicle light control command is generated based on the vehicle light scene requirement information.

[0130] Once the headlight control command is generated, the headlights of the first vehicle are immediately turned on or off based on the headlight control command.

[0131] For example, for the i-th location among at least one location point, calculate the i-th distance between the vehicle position of the first vehicle and the i-th location point. i is a positive integer.

[0132] In response to the i-th distance being less than or equal to a preset distance threshold, it is determined that the i-th position point and the vehicle position of the first vehicle meet the preset instruction generation conditions.

[0133] The vehicle lighting scenario requirements include control types for controlling vehicle lights at at least one location point. These control types include turning off vehicle lights, turning on vehicle lights, and keeping the vehicle lights in the same state.

[0134] Based on the control type for controlling the headlights at position i in the headlight scenario requirements information, generate the headlight control command corresponding to position i.

[0135] For example, if the distance between the first vehicle's position and the i-th position point is less than or equal to a preset distance threshold, it means that the first vehicle is about to pass the i-th position point. The preset distance threshold is 10 meters, and the i-th distance is 9 meters.

[0136] If the vehicle headlight scenario requirement information indicates that the headlights need to be turned on when passing the i-th position point, then a headlight control command is generated to turn on the headlights in advance, ensuring that the lighting conditions for the first vehicle when passing the i-th position point meet the requirements for driving safety.

[0137] Specifically, when the distance between the first vehicle and the i-th position point is a preset sensor detection distance, the light sensor is activated to collect the light intensity near the i-th position point. Based on the light intensity, it is determined whether the control type for controlling the headlights at the i-th position point in the headlight scene requirement information needs to be corrected. If correction is required, the control type for controlling the headlights is determined based on the light intensity collected by the light sensor. If correction is not required, instructions are generated based on the control type for controlling the headlights at the i-th position point in the headlight scene requirement information.

[0138] Among them, the preset sensor detection distance is greater than the preset distance threshold. When the i-th distance is less than or equal to the preset distance threshold, a vehicle light control command is generated based on the corrected vehicle light control type. After the vehicle light control command is generated, the vehicle light is controlled immediately.

[0139] When the first vehicle passes the i-th position, if there is an i+1-th position and the distance between the i+1-th position and the i-th position is greater than a preset distance threshold, the vehicle lights will remain at the i-th position until the distance between the first vehicle and the i+1-th position is only the preset distance threshold. Then, the vehicle lights control command corresponding to the i+1-th position will be generated based on the vehicle lights control information for the i+1-th position in the vehicle lights scenario requirement information.

[0140] For example, the distance between position i and position i+1 is 20 meters, and the preset distance threshold is 10 meters. After the first vehicle passes position i, it continues to maintain the headlight state until the distance between the first vehicle and position i+1 equals the preset distance threshold. Based on the control type for headlight control at position i+1 in the headlight scenario requirement information, a headlight control command corresponding to position i+1 is generated to control the headlight state.

[0141] In summary, the vehicle headlight control method provided in this application can predict road conditions and lighting conditions in the section of road the first vehicle is about to pass through by collecting environmental information. It can then determine whether there are scenarios requiring the headlights to be turned on or off, providing sufficient time for advance activation or deactivation, thus achieving automatic headlight control. The entire process of information collection and headlight control command generation is executed on the vehicle itself, eliminating the need to send collected information to the cloud for analysis and decision-making, and then have the cloud return the decision results to the vehicle for headlight control. This reduces the risk of information leakage during transmission, improves data security, reduces transmission time for information and decision results, and increases decision-making and headlight control efficiency. In extreme situations, it can automatically activate or deactivate headlights promptly when road conditions or lighting conditions change, ensuring driver safety.

[0142] Figure 4 This is a structural block diagram of a vehicle lighting control device provided in an exemplary embodiment of this application, such as... Figure 4 As shown, the device includes the following parts.

[0143] The acquisition module 410 is used to acquire environmental information in real time, and the environmental information is used to describe the environment of the road where the first vehicle is located.

[0144] Prediction module 420 is used to predict road condition information based on the environmental information, wherein the road condition information is used to indicate the road conditions and lighting conditions of the first road segment not yet traveled on the driving path.

[0145] Analysis module 430 is used to analyze the road condition information to obtain vehicle lighting scene demand information in the first road segment. The vehicle lighting scene demand information is used to describe the vehicle lighting control requirements of the first vehicle in the first road segment.

[0146] The control module 440 is used to generate vehicle lighting control commands based on the vehicle lighting scene requirement information and the environmental information, and to control the vehicle lights of the first vehicle based on the vehicle lighting control commands.

[0147] In an optional embodiment, the illumination conditions include illumination values ​​at multiple locations along the first road segment;

[0148] The analysis module 430 is configured to, in response to the illumination conditions where the illumination value corresponding to a first location point is lower than a preset first illumination threshold, determine that the vehicle lighting scene requirement information includes turning on the vehicle lights or keeping the vehicle lights on at the first location point; or, in response to the illumination conditions where the illumination value corresponding to a second location point is higher than a preset second illumination threshold, determine that the vehicle lighting scene requirement information includes turning off the vehicle lights or keeping the vehicle lights off at the second location point; or, in response to the illumination conditions where the illumination value corresponding to a third location point is higher than the first illumination threshold and lower than the second illumination threshold, determine that the vehicle lighting scene requirement information includes keeping the vehicle lights unchanged at the third location point.

[0149] In an optional embodiment, the vehicle headlight scene requirement information further includes vehicle headlight brightness information, which is used to indicate the headlight brightness when the headlights of the first vehicle are turned on.

[0150] The analysis module 430 is further configured to calculate a first difference between the illumination value corresponding to the first location point and the first illumination threshold; and to obtain the corresponding headlight brightness information based on the first difference.

[0151] In an optional embodiment, the traffic information includes lighting conditions, weather type, and road type corresponding to multiple location points in the first road segment;

[0152] The analysis module 430 is further configured to obtain a preset vehicle headlight scene mapping table, which includes a first mapping relationship between various lighting conditions and various vehicle headlight control scenarios, a second mapping relationship between various weather types and the various vehicle headlight control scenarios, and a third mapping relationship between various road types ahead and the various vehicle headlight control scenarios; based on the road condition information, the target vehicle headlight control scenarios corresponding to the multiple location points are determined from the vehicle headlight scene mapping table to obtain vehicle headlight scene demand information within the first road segment.

[0153] In an optional embodiment, the control module 440 is further configured to update the environmental information in real time to obtain the environmental information at the current moment, wherein the environmental information at the current moment includes the vehicle position of the first vehicle; wherein the vehicle lighting scene requirement information includes at least one location point in the first road segment where there is a vehicle lighting control requirement; and if the at least one location point and the vehicle position of the first vehicle meet a preset instruction generation condition, the vehicle lighting control instruction is generated based on the vehicle lighting scene requirement information.

[0154] In an optional embodiment, the control module 440 is further configured to calculate, for the i-th location among the at least one location points, the i-th distance between the vehicle position of the first vehicle and the i-th location point; i is a positive integer; in response to the i-th distance being less than or equal to a preset distance threshold, determine that the i-th location point and the vehicle position of the first vehicle meet a preset instruction generation condition; wherein, the headlight scene requirement information includes a control type for controlling the headlights at the at least one location point, the control type including turning off the headlights, turning on the headlights, and keeping the headlight state unchanged; based on the control type for controlling the headlights at the i-th location point in the headlight scene requirement information, generate a headlight control instruction corresponding to the i-th location point.

[0155] In an optional embodiment, the traffic information includes the road type of the first road segment and the illumination values ​​of multiple locations within the first road segment;

[0156] The analysis module 430 is further configured to obtain a preset coefficient table, which includes safety coefficients corresponding to various road types. The values ​​of the safety coefficients describe the safety level of vehicles traveling on different types of roads, and the values ​​of the safety coefficients are positively correlated with the safety level. Based on the road type of the first road segment, a first coefficient is obtained from the coefficient table. The first coefficient indicates the safety level of the first vehicle traveling on the first road segment. Multiple values ​​corresponding to the multiple location points are obtained by multiplying the illumination values ​​of the multiple location points with the first coefficient. Based on the multiple values, the vehicle lighting scene corresponding to the multiple location points is determined to obtain the vehicle lighting scene requirement information.

[0157] In an optional embodiment, the analysis module 430 is further configured to, in response to the presence of at least one value among the plurality of values ​​that meets a preset headlight turning-on requirement, determine at least one location point corresponding to each of the at least one value, and determine that the headlight scene requirement information includes turning on the headlights or keeping the headlights on at the at least one location point.

[0158] In summary, the vehicle headlight control device provided in this application can predict road conditions and lighting conditions in the section of road the first vehicle is about to pass through by collecting environmental information. It can then determine whether there are scenarios requiring the headlights to be turned on or off, providing sufficient time for advance activation or deactivation and achieving automatic headlight control. The entire process of information collection and headlight control command generation is executed on the vehicle side, eliminating the need to send collected information to the cloud for analysis and decision-making, and then have the cloud return the decision results to the vehicle for headlight control. This reduces the risk of information leakage during transmission, improves data security, reduces transmission time for information and decision results, and increases decision-making and headlight control efficiency. In extreme situations, it can automatically activate or deactivate headlights promptly when road conditions or lighting conditions change, ensuring driver safety.

[0159] It should be noted that the vehicle light control device provided in the above embodiments is only an example of the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the vehicle light control device and the vehicle light control method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0160] Figure 5 This illustration shows a structural block diagram of a computer device 500 provided in an exemplary embodiment of this application. The computer device 500 may be a smartphone, tablet computer, MP3 player (Moving Picture Experts Group Audio Layer III), MP4 player (Moving Picture Experts Group Audio Layer IV), laptop computer, or desktop computer. The computer device 500 may also be referred to as a user device, portable terminal, laptop terminal, desktop terminal, or other names.

[0161] Typically, computer device 500 includes a processor 501 and a memory 502.

[0162] Processor 501 may include one or more processing cores, such as a quad-core processor, an octa-core processor, etc. Processor 501 may be implemented using at least one hardware form selected from DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). Processor 501 may also include a main processor and a coprocessor. The main processor, also known as a CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, processor 501 may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, processor 501 may also include an AI (Artificial Intelligence) processor, which is used to handle computational operations related to machine learning.

[0163] The memory 502 may include one or more computer-readable storage media, which may be non-transitory. The memory 502 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments, the non-transitory computer-readable storage media in the memory 502 are used to store at least one instruction, which is executed by the processor 501 to implement the vehicle lighting control method provided in the method embodiments of this application.

[0164] In some embodiments, the computer device 500 also includes other components 503, the type and number of which can be selected based on the functional needs of the computer device 500. Those skilled in the art will understand that... Figure 5 The structure shown does not constitute a limitation on the computer device 500, and may include more or fewer components than shown, or combine certain components, or use different component arrangements.

[0165] Optionally, the computer-readable storage medium may include: read-only memory (ROM), random access memory (RAM), solid-state drives (SSDs), or optical discs, etc. The random access memory may include resistive random access memory (ReRAM) and dynamic random access memory (DRAM). The sequence numbers of the embodiments in this application are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0166] This application also provides a computer device, which includes a processor and a memory. The memory stores at least one instruction, at least one program, a code set, or an instruction set. The at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the vehicle light control method as described in any of the above embodiments of this application.

[0167] This application also provides a computer-readable storage medium storing at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement the vehicle light control method as described in any of the above embodiments of this application.

[0168] This application also provides a computer program product or computer program that includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform any of the vehicle light control methods described in the above embodiments.

[0169] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0170] The above description is merely an optional embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A vehicle headlight control method, characterized in that, The method includes: Real-time collection of environmental information, which is used to describe the environment of the road where the first vehicle is located; Based on the environmental information, road condition information is predicted. The road condition information is used to indicate the road conditions and lighting conditions of the first road segment that has not been traveled on the driving path. The road condition information includes the road type of the first road segment and the lighting values ​​of multiple locations in the first road segment. Obtain a preset coefficient table, which includes safety coefficients corresponding to various road types. The values ​​of the safety coefficients are used to describe the safety level of vehicles when driving on different types of roads, and the values ​​of the safety coefficients are positively correlated with the safety level. A first coefficient is obtained from the coefficient table based on the road type of the first road segment. The first coefficient is used to indicate the safety level of the first vehicle when driving in the first road segment. Multiple values ​​corresponding to the multiple location points are obtained by multiplying the illumination values ​​of the multiple location points with the first coefficient; Based on the multiple values, the vehicle lighting scene corresponding to the multiple location points is determined to obtain vehicle lighting scene requirement information. The vehicle lighting scene requirement information is used to describe the vehicle lighting control requirements of the first vehicle in the first road segment. Based on the vehicle lighting scenario requirements information and the environmental information, a vehicle lighting control command is generated, and the vehicle lighting of the first vehicle is controlled based on the vehicle lighting control command.

2. The method according to claim 1, characterized in that, The vehicle headlight scenario requirement information also includes vehicle headlight brightness information, which is used to indicate the headlight brightness when the headlights of the first vehicle are turned on; the method further includes: Calculate the first difference between the illumination value corresponding to the first location point and the first illumination threshold; The corresponding headlight brightness information is obtained based on the first difference.

3. The method according to claim 1 or 2, characterized in that, The road condition information also includes the lighting conditions, weather type, and road type corresponding to multiple locations in the first road segment; The method further includes: Obtain a preset vehicle light scene mapping table, which includes a first mapping relationship between various lighting conditions and various vehicle light control scenes, a second mapping relationship between various weather types and the various vehicle light control scenes, and a third mapping relationship between various road types ahead and the various vehicle light control scenes. Based on the road condition information, the target vehicle light control scenarios corresponding to the multiple location points are determined from the vehicle light scene mapping table to obtain the vehicle light scene demand information in the first road segment.

4. The method according to claim 1 or 2, characterized in that, The process of generating vehicle lighting control commands based on the vehicle lighting scenario requirements information and the environmental information includes: The environmental information is updated in real time to obtain the environmental information at the current moment, which includes the vehicle position of the first vehicle; wherein, the vehicle lighting scene requirement information includes at least one location point in the first road segment where there is a vehicle lighting control requirement; If a preset instruction generation condition is met between the at least one location point and the vehicle position of the first vehicle, the vehicle light control instruction is generated based on the vehicle light scene requirement information.

5. The method according to claim 4, characterized in that, When a preset instruction generation condition is met between the at least one location point and the vehicle position of the first vehicle, the vehicle light control instruction is generated based on the vehicle light scene requirement information, including: For the i-th position among the at least one position point, calculate the i-th distance between the vehicle position of the first vehicle and the i-th position point; i is a positive integer; In response to the i-th distance being less than or equal to a preset distance threshold, it is determined that the i-th location point and the vehicle position of the first vehicle meet preset instruction generation conditions; wherein, the vehicle light scene requirement information includes a control type for controlling the vehicle lights at the at least one location point, and the control type includes turning off the vehicle lights, turning on the vehicle lights, and keeping the vehicle lights unchanged; Based on the control type for controlling the headlights at the i-th position point in the headlight scenario requirement information, a headlight control command corresponding to the i-th position point is generated.

6. The method according to claim 1 or 2, characterized in that, The process of determining the vehicle lighting scene corresponding to each of the multiple location points based on the multiple numerical values, and obtaining vehicle lighting scene requirement information, includes: In response to the fact that at least one of the plurality of values ​​meets the preset requirements for turning on the headlights, at least one position point corresponding to each of the at least one value is determined, and the headlight scene requirement information is determined to include turning on the headlights or keeping the headlights on at the at least one position point.

7. A vehicle lighting control device, characterized in that, The device includes: The data acquisition module is used to collect environmental information in real time, and the environmental information is used to describe the environment of the road where the first vehicle is located. The prediction module is used to predict road condition information based on the environmental information. The road condition information is used to indicate the road conditions and lighting conditions of the first road segment that has not been traveled on the driving path. The road condition information includes the road type of the first road segment and the lighting values ​​of multiple locations in the first road segment. The analysis module is used to obtain a preset coefficient table, which includes safety coefficients corresponding to various road types. The values ​​of the safety coefficients describe the safety level of a vehicle driving on different types of roads, and the values ​​of the safety coefficients are positively correlated with the safety level. Based on the road type of the first road segment, a first coefficient is obtained from the coefficient table. The first coefficient indicates the safety level of the first vehicle driving on the first road segment. Multiple values ​​corresponding to the multiple location points are obtained by multiplying the illumination values ​​of the multiple location points with the first coefficient. Based on the multiple values, the headlight scenes corresponding to the multiple location points are determined to obtain headlight scene requirement information, which describes the headlight control requirements of the first vehicle on the first road segment. The control module is used to generate vehicle lighting control commands based on the vehicle lighting scene requirement information and the environmental information, and to control the vehicle lights of the first vehicle based on the vehicle lighting control commands.

8. A computer device, characterized in that, The computer device includes a processor and a memory, the memory storing at least one program, which is loaded and executed by the processor to implement the vehicle lighting control method as described in any one of claims 1 to 6.

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