A smart automotive lighting assist control system and method

Abstract

This invention discloses an intelligent automotive lighting assistance control system and method. The system includes an environmental sensing module, a system control module, and a system execution module. The environmental sensing module collects vehicle driving status data and environmental data, and its output is connected to the system control module. The system control module generates lighting control signals based on the vehicle driving status data and environmental data, and its output is connected to the system execution module. The system execution module receives and executes the lighting control signals to control the vehicle's lighting. This invention achieves automatic vehicle lighting control through automatic identification, thereby meeting users' needs for vehicle automation and intelligence.

Description

Technical Field

[0001] This invention relates to the field of automotive lighting control, and in particular to an intelligent automotive lighting auxiliary control method and system. Background Technology

[0002] With the development of the automotive industry and the increasing awareness of traffic safety, the importance of automotive lighting systems has become increasingly prominent. Traditional automotive lighting systems typically include headlights, taillights, turn signals, fog lights, etc. The main function of these lighting systems is to provide illumination and signals to ensure driving safety at night. However, traditional lighting systems have a low level of automation and intelligence, and cannot adaptively adjust according to real-time traffic conditions, weather conditions, and road conditions, affecting driving safety and ride comfort.

[0003] Currently, some intelligent lighting systems have emerged on the market. These systems can automatically adjust the high and low beams to a certain extent according to changes in the environment. However, this adjustment is far from meeting users' needs for intelligent lighting adjustment. Automotive lighting includes interior lights and exterior lights. Whether it is interior lights or exterior lights, the main solution is to manually control the lights by the people inside the car. It cannot adaptively complete the adjustment and control of the lights and cannot meet the needs of intelligent and automated vehicles. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide an intelligent vehicle lighting auxiliary control method and system, which realizes automatic vehicle lighting control through automatic identification, thereby meeting users' needs for vehicle automation and intelligence.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: an intelligent automotive lighting auxiliary control system, comprising an environmental sensing module, a system control module, and a system execution module; wherein the environmental sensing module is used to collect vehicle driving status data and driving environment data, and its output terminal is connected to the system control module; the system control module is used to generate lighting control signals based on the vehicle driving status data and driving environment data, and its output terminal is connected to the system execution module; the system execution module receives the lighting control signals and executes them to control the vehicle lights.

[0006] The environmental sensing module includes an on-board sensor, which collects vehicle driving data and environmental data of the vehicle's location.

[0007] The system control module includes a central processing unit (CPU) and an algorithm unit. It receives data transmitted from the sensing module, analyzes and processes the data based on a preset algorithm, and generates corresponding control signals.

[0008] The system execution module includes a vehicle lighting system and an interior lighting system; the vehicle lighting system and the interior lighting system receive control signals and execute them to control the exterior lights and / or the interior lighting system.

[0009] The control system also includes a system communication module, which is connected to the system control module. The system communication module is used to enable communication between vehicles or between vehicles and traffic equipment to assist in vehicle lighting control.

[0010] The system control module contains an image processing algorithm that runs in its algorithm unit. This algorithm analyzes and identifies the vehicle's environment using image data of the vehicle's surroundings. The image processing algorithm includes a neural network model for image recognition and classification.

[0011] A method for intelligent automotive lighting auxiliary control includes: collecting ambient light intensity data of the vehicle while it is driving; when the ambient light intensity value of the vehicle's driving environment is lower than a set threshold, the system control module is activated; the system control module collects ambient data and vehicle status data of the vehicle in real time and generates a corresponding control system to the system execution module; the system execution module executes the control signal and realizes auxiliary control of the lights.

[0012] The control signals generated by the system control module include headlight control signals and interior lighting control signals, which are sent to the headlight system and interior lighting system respectively to achieve separate control of the interior and exterior lights.

[0013] After the system control module starts working, it interacts with surrounding vehicles or traffic equipment through the system communication module and controls the vehicle lights based on the information exchanged.

[0014] During the lighting auxiliary control process, the system control module acquires the adjustment signal entered by the user through the manual switch in real time. After receiving the lighting adjustment signal entered by the user through the manual switch, the system control module executes the user's adjustment signal and holds it for a set time before starting the lighting auxiliary control function of the system control module.

[0015] The advantages of this invention are: it can adjust the headlights according to the external environment and vehicle status, and it is linked with the navigation system. When the navigation system detects a tunnel ahead, it turns on the headlights in advance; when the road ahead is narrow or there are obstacles, it automatically switches to low beams. In summary, this invention provides a method and system for intelligent automotive lighting assistance control. This system automatically adjusts vehicle lighting by collecting ambient light intensity, vehicle speed, road condition information, and driver status data, including turning headlights on and off, switching between high and low beams, and controlling auxiliary lights. This significantly improves driving safety and comfort, and facilitates innovation in automotive intelligent lighting models and technical frameworks. Attached Figure Description

[0016] The following is a brief explanation of the contents of each of the accompanying drawings and the markings in the drawings:

[0017] Figure 1 This is a block diagram of the automotive intelligent lighting auxiliary control system provided by the present invention.

[0018] Figure 2 This is a diagram of the multi-scale feature fusion network module provided by the present invention.

[0019] Figure 3 This is a diagram of the triple attention module provided by the present invention. Detailed Implementation

[0020] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings and the description of the preferred embodiments.

[0021] Example 1:

[0022] This embodiment mainly implements auxiliary control of vehicle intelligent lighting to meet users' needs for intelligent and automated lighting control, reducing the trouble caused by manual operation. The specific solution is as follows:

[0023] An intelligent automotive lighting assistance control system, the hardware of which includes an environmental sensing module, a system control module, and a system execution module;

[0024] The environmental sensing module is used to collect vehicle driving status data and driving environment data, and its output is connected to the system control module.

[0025] The system control module generates lighting control signals based on vehicle driving status data and driving environment data. Its output is connected to the system execution module. The system execution module receives the lighting control signals and executes them to control the vehicle lights.

[0026] The environmental sensing module includes onboard sensors that collect vehicle driving data and environmental data, including but not limited to fiber optic sensors, radar processing systems, ADAS cameras, speed sensors, and steering angle sensors. The environmental data collected includes but is not limited to ambient light intensity, weather conditions, vehicle speed, and steering angle. This data can be categorized into vehicle environmental parameter data, such as light intensity information, or vehicle driving status data, such as vehicle driving data and vehicle control status.

[0027] The system control module includes a central processing unit (CPU) and an algorithm unit. It receives data from the sensor module, analyzes and processes the data based on a preset algorithm, and generates corresponding control signals. The CPU is the core processor, and its internal algorithm unit implements algorithms based on the collected data to obtain corresponding lighting auxiliary control strategies and generate corresponding lighting control signals.

[0028] The system execution module includes the vehicle lighting system and the interior lighting system. The vehicle lighting system and the interior lighting system receive control signals and execute them to control the exterior lights and / or interior lighting systems. Both the vehicle lighting system and the interior lighting system are implemented through onboard controllers, typically using a Body Control Module (BCM). The BCM receives control signals and converts them into lighting control commands. The outputs of the BCM are connected to the vehicle's headlights and interior lighting systems respectively, enabling control of the headlights and / or interior lighting.

[0029] In a preferred embodiment, the control system further includes a system communication module. The system control module is connected to the system communication module, which enables communication between vehicles or between vehicles and traffic equipment to assist in vehicle lighting control. Each vehicle is equipped with a corresponding system communication module, thus realizing interactive control between vehicles. Simultaneously, with communication-enabled devices installed on traffic equipment, interactive control can also be achieved. Based on this interactive control, the vehicle's lighting control is implemented, meeting the requirements of automation and intelligence.

[0030] In this scheme, the algorithm unit in the system control module runs an image processing algorithm, which is used to analyze and identify the environmental data of the vehicle through image data of the vehicle environment. The image processing algorithm includes a neural network model to identify and classify images.

[0031] The control method of the automotive intelligent lighting assistance control system based on the above hardware includes the following steps: acquiring ambient light intensity data of the vehicle's driving environment; when the ambient light intensity value of the vehicle's driving environment is lower than a set threshold, the system control module is activated; the system control module acquires environmental data and vehicle status data in real time and generates corresponding control system data, which is sent to the system execution module; the system execution module executes the control signals and realizes auxiliary control of the lights. The control signals generated by the system control module include headlight control signals and in-vehicle lighting control signals, which are respectively sent to the headlight system and the in-vehicle lighting system to achieve separate control of the interior and exterior lights.

[0032] Its control strategies include:

[0033] When the ambient light intensity falls below the set value, the system control module activates the headlight assist control function. Once activated, this function generates corresponding headlight assist control signals based on environmental data and vehicle status data to control the illumination and brightness of the headlights. The environmental sensing module detects light intensity data using photoelectric sensors, the ADAS camera detects lane departure, the steering angle sensor measures the steering wheel angle, and the radar processing system processes the distance to surrounding vehicles.

[0034] After the headlight assist control function is activated, the brightness of the vehicle's headlights is adjusted based on the collected light intensity data. A control signal for the headlight brightness is generated and sent to the BCM (Battery Management System), which then drives the headlight illumination control. When the rain sensor detects that the vehicle is driving in rainy conditions, it collects real-time rainfall change signals. When an increase in rainfall is detected (i.e., a gradual increase in rainfall per unit time), the system control module generates control commands to increase headlight brightness, as well as commands to activate and increase fog light brightness. These commands are used to control the illumination and brightness of the headlights and fog lights, respectively. The brightness of the headlights and fog lights is controlled based on the amount of rainfall per unit time.

[0035] After the headlight assist control function is activated, the vehicle's headlights turn on and are at the initial headlight beam angle position. At this initial position, the beam angle remains constant, meeting the straight-line driving angle required by the user at speeds below 60 km / h. The system collects real-time data on vehicle speed and steering wheel angle, adjusting the headlight beam angle based on these factors. It also controls the angle between the headlight beam direction and the horizontal direction based on vehicle speed, and adjusts the headlights left and right based on the steering wheel angle to move the beam tubes laterally. The angle between the headlight beam direction and the horizontal direction gradually decreases to a set threshold as vehicle speed increases. Under normal circumstances, the angle 'w' between the headlight beam and the horizontal is constant. That is, when the headlight shines forward onto the ground in front of the vehicle, there is an angle with the horizontal. Generally, this illumination range meets the user's driving needs, typically several tens of meters. However, as vehicle speed increases, the user's reaction time to obstacles decreases. Therefore, increasing the driver's forward visibility in line with speed can reduce accidents. Thus, increasing the headlight beam distance with increasing speed is essential. For example, the illumination distance differs between city roads and highways due to different speeds. Adjusting the headlight beam distance according to vehicle speed ensures driving safety. This distance is achieved by adjusting the angle of the headlights. Gradually decreasing the angle 'w' between the headlight and the horizontal plane increases the beam distance. Simultaneously, while decreasing the angle 'w', the headlight brightness is increased. The increase in headlight brightness has a linear or non-linear one-to-one relationship with the decrease in angle 'w', with the headlight brightness gradually increasing as 'w' decreases.

[0036] In a preferred embodiment, the switching between high and low beams can be determined by data collected from an ADAS camera, GPS sensor, vehicle speed sensor, etc. Specifically, when the speed is greater than 60-80 km / h and GPS sensor analysis indicates the vehicle is on a highway, the high beams are switched; otherwise, the low beams are switched. The ADAS camera collects signals from vehicles ahead and / or their high / low beam switching signals. Upon detecting either signal, the high beams are briefly switched to low beams and held for a set time before reverting to high beams.

[0037] In a preferred embodiment of this application, the brake light signal of the vehicle in front is collected and identified by an ADAS camera. The rear image of the vehicle in front is also collected by the ADAS camera. The image recognition algorithm is used to identify whether the brake light of the vehicle is lit. When the brake light in front is lit, the distance to the vehicle in front is obtained by the vehicle's radar system. When the distance to the vehicle in front is lower than a set threshold, a headlight control signal is generated to adjust the lights, reduce the brightness of the headlights and activate the vehicle's hazard lights to remind the vehicle behind, and output an alarm signal to the driver to remind the driver to pay attention to safety when the vehicle in front brakes suddenly.

[0038] In a preferred embodiment of this application, image analysis and GPS positioning data collected by an ADAS camera are used to analyze the road type on which the vehicle is traveling, identify the road type, and adjust the illumination range and brightness of the vehicle's headlights based on the road type to match different illumination effects and requirements.

[0039] The vehicle monitors the driver's condition using an in-car camera, while a driver status sensor tracks the driver's eye movements and fatigue levels. When driver fatigue is detected, the reading lights inside the vehicle are activated to alert the driver and prompt them to make corrections. Since the interior lighting is dim at night, prolonged darkness can cause driver fatigue and drowsiness. Activating the reading lights illuminates the interior, and the sudden light stimulation can briefly wake the driver's attention and remind them to make corrections, thus preventing accidents caused by drowsy driving.

[0040] The system control module connects to the navigation system to obtain real-time information about the vehicle's driving environment. When the navigation system detects a tunnel ahead, it activates the headlights in advance. When the road ahead is narrow or there are obstacles, it automatically switches to low beams and maintains this position until it leaves the narrow or obstructed area, then returns to the headlight state before encountering the obstacle.

[0041] In a preferred embodiment of this application, after the system control module starts working, it interacts with surrounding vehicles or traffic equipment through the system communication module, and controls the vehicle lights based on the interactive information. For vehicle-to-vehicle communication, when both the preceding and following vehicles are equipped with system communication modules, the braking signal of the preceding vehicle is transmitted to the system communication module of the following vehicle through the system communication module, and then to the system control module. After receiving the braking signal from the preceding vehicle, the system control module determines the emergency braking situation ahead based on the rate of change of the brake pedal and / or the rate of change of the vehicle speed after braking. When the rate of change of the brake pedal or the rate of change of the vehicle speed exceeds a set threshold, an emergency braking situation is determined. At this time, after the following vehicle recognizes the emergency braking situation ahead, it drives and controls its own headlights to perform multiple high / low beam switching controls to facilitate the driver's observation of the vehicle ahead, and simultaneously controls its own hazard lights to illuminate to remind the following vehicle to ensure safety, thus achieving safe and reliable lighting, adjusting the lighting status in advance, and ensuring driving safety.

[0042] In a preferred embodiment of this application, the priority of the headlight auxiliary control function is lower than that of the user's manual control. During headlight auxiliary control, the system control module acquires the adjustment signal input by the user via manual switch in real time. Upon receiving the user's adjustment signal, the system control module executes the user's adjustment signal and maintains it for a set time before activating the headlight auxiliary control function. When the user manually controls the lights, the user's command is executed with the highest priority. The system control module pauses the headlight auxiliary control function for a set time. During this set time, the headlight auxiliary control function is not activated, thus satisfying the user's active control objective. After the set time expires, the headlight auxiliary control function is resumed, thus satisfying the user's high control priority requirement and achieving the user's active vehicle control objective while maintaining compatibility with the auxiliary control function. The two functions do not conflict, maximizing the fulfillment of the user's control needs.

[0043] Example 2:

[0044] This invention provides an intelligent automotive lighting auxiliary control method and system. This system can automatically adjust vehicle lights based on factors such as ambient light, vehicle speed, road conditions, and driver status, solving problems such as untimely manual adjustment and unreasonable lighting control in existing technologies, thus improving driving safety and comfort. The invention provides an intelligent automotive lighting auxiliary control method and system, comprising the following components: An environmental sensing module: including a radar processing system, ADAS camera, speed sensor, steering angle sensor, etc., used to collect data such as ambient light intensity, weather conditions, vehicle speed, and steering angle in real time. A system control module: including a central processing unit (CPU) and an algorithm unit, receiving data transmitted from the sensing module, analyzing and processing the data based on a preset algorithm, and generating corresponding control signals. A system execution module: including headlights, taillights, turn signals, fog lights, etc., which adjust the brightness, angle, and on / off status of the lights in real time through signals from the control module. A system communication module: including an in-vehicle network communication unit, capable of exchanging information with other vehicles or traffic facilities to further optimize the lighting control strategy.

[0045] Environmental Perception: The sensing module monitors ambient light intensity, weather conditions, vehicle speed, and steering angle in real time and transmits the data to the control module. Data Processing: The central processing unit of the control module analyzes the received data and, based on a preset algorithm model, determines whether the current lighting status needs adjustment. Lighting Adjustment: The control module generates corresponding control signals and transmits them to the execution module to adjust the brightness and angle of the headlights, control the on / off state of the taillights and turn signals, and activate / deactivate the fog lights. Information Interaction: Through the communication module, the intelligent lighting system can interact with other vehicles or traffic facilities, such as receiving braking signals from vehicles ahead to adjust the lighting status in advance and ensure driving safety.

[0046] like Figure 1As shown, in this embodiment, the automotive intelligent lighting system includes an environmental sensing module, a system control module, a system execution module, and a system communication module. The environmental sensing module uses photoelectric sensors to detect light intensity data; an ADAS camera detects lane departure information; a steering angle sensor measures the steering wheel angle; a radar processing system processes the distance to surrounding vehicles; and the ADAS camera feeds captured images back to a multi-scale feature fusion module and a triple attention module to simultaneously acquire confidence scores for information weighting. The system control module includes a central processing unit and an algorithm unit. The central processing unit receives data from the sensing module, and the algorithm unit analyzes and processes the data based on a preset algorithm model to generate corresponding control signals. The system execution module includes headlights, taillights, turn signals, and fog lights. The headlights adjust brightness and angle via control signals, the taillights and turn signals are controlled by control signals to maintain their on / off state, and the fog lights are controlled by control signals to start / stop. The system communication module includes an in-vehicle network communication unit, enabling information interaction with other vehicles or traffic facilities.

[0047] In this embodiment, when the vehicle is driving at night or in low-light conditions, the light sensor detects that the ambient light intensity is below a set value. The sensor data is transmitted to the control module, which then uses an algorithm unit to generate a control signal to increase the brightness of the headlights. Upon receiving the control signal, the headlights automatically increase their brightness to ensure visibility of the road ahead. In rainy conditions, the rain sensor detects increased rainfall, and the sensor data is transmitted to the control module. The control module then uses an algorithm unit to generate a control signal to turn on the fog lights. Upon receiving the control signal, the fog lights automatically turn on, improving the vehicle's visibility in rainy weather. The vehicle speed sensor and steering angle sensor detect the vehicle's speed and steering angle, and the data is transmitted to the control module. The control module uses an algorithm unit to generate a control signal to adjust the headlight angle. Upon receiving the control signal, the headlights automatically adjust their illumination angle, making the lighting of the road ahead more precise. The communication module enables the intelligent lighting system to interact with other vehicles or traffic facilities. For example, when it detects that a vehicle ahead is braking, the system adjusts the lighting status in advance, reducing the headlight brightness or issuing a warning signal, further improving driving safety. This embodiment also includes remote monitoring and management functions based on an in-vehicle network. Through the in-vehicle network communication unit, the intelligent lighting system can connect to the owner's mobile application, allowing the owner to monitor the vehicle's lighting status in real time and manually adjust it as needed. Simultaneously, the system can also set different lighting modes according to the owner's personalized needs, such as energy-saving mode and sport mode, enhancing the user experience. In this embodiment, the algorithm of the intelligent lighting system has been further optimized, enabling it to adaptively adjust to more environmental factors. For example, a road type recognition function based on GPS data has been added, allowing the system to automatically adjust the headlight's illumination range and brightness according to road type, such as urban roads, highways, and rural roads, providing a more suitable lighting effect.

[0048] The in-vehicle image processing unit performs recognition and classification using a neural network model. It replaces some standard convolutions in the encoder with pyramid convolutions to extract and aggregate effective information from different receptive fields. The pyramid convolution model contains convolutional kernels of varying sizes and depths, enabling it to capture different levels of image detail, reduce kernel depth, and improve feature map fusion efficiency. In the processing of the integrated convolutional kernel output, pyramid convolutions acquire supplementary kernel information, further enhancing the model's recognition performance. Figure 2 As shown in (a), the SE module enhances the utilization of effective information by adaptively controlling the weight of each channel. The SE module formula is as follows:

[0049] S = F scale (F ex (F sq (X)),X)

[0050] Where F sq (·) represents the squeeze operation implemented by global average pooling, F ex (·) represents the activation operations implemented by the fully connected layer with ReLU and Sigmoid activation functions, respectively, F scale (·) indicates a channel multiplication operation. The Residual module structure is as follows: Figure 2 As shown in (b), the Residual module consists of two 3×3 convolutional kernels and a skip connection for feature reuse. The module is implemented through batch normalization. Then, the aggregated features are processed by ReLU activation.

[0051] R = g r (g b (f3(g r (g b (f3(X)))))+g b (f1(X)))

[0052] Where f n (·) represents a convolutional layer with a kernel size of n×n, g b For batch normalization operation BN, g r The ReLU activation function is used. The multi-scale feature fusion module is as follows: Figure 2 As shown in (c), the main task of the multi-scale feature fusion module is to enable the module to obtain a more suitable receptive field through adaptive processing and improve the feature learning ability of the model across multiple image scales. Simultaneously, to reduce the number of network parameters, consecutive multi-pyramid convolutional kernels of different sizes are used after the 1×1 convolutional layer to replace the 5×5 or 7×7 convolutional kernels. The multi-scale feature fusion module is represented by the following equation:

[0053] I = g r (g b (f1(g c (f1(X),f1(p3(X)),y3(f1(X)),y3(y3(f1(X))),X))+X))

[0054] Y = S(I(R(X)))

[0055] Where p n (·) represents the average pooling layer AP with a kernel size of n×n, and y3(·) represents the pyramid convolution. R(·), S(·), and Y represent the outputs of the Residual module, the SE module, and the multi-scale feature fusion module, respectively.

[0056] The triple attention module captures cross-dimensional interaction information through three branches, effectively establishing dependencies between each image pixel channel by calculating attention weights. For example... Figure 3As shown, the triple attention module constructs content connections between image dimensions through rotation and residual transformation. The first two branches mainly complete cross-channel interaction between channel dimension C and spatial dimension W or H. The permutation completes the correlation between channels and arbitrary spatial dimensions through rotation processing. The first branch mainly transforms the image across dimensions into a feature tensor with a W×H×C structure. Then, it performs max pooling and average pooling on the input feature map at the W-dimensional position. Through max pooling and average pooling operations, the feature structure of the C-dimensional dimension is processed into a two-dimensional tensor. After processing, the two-dimensional tensor outputs a 2×H×C feature shape. Then, it is processed by a 3×3 convolutional layer, a regularization layer and a sigmoid activation function. Finally, the image feature dimensions C and H are permuted into C×H×W-dimensional features. The second branch also performs a cross-dimensional transformation on the image, transforming it into a feature tensor of shape H×C×W. Then, max pooling and average pooling are performed along the H dimension to reduce the image features in channel dimension C to a two-dimensional structure. A 3×3 convolution and regularization layer are then used to process the feature tensor, which is then passed through a sigmoid activation function to obtain the attention weight tensor. The image dimensions C and H are swapped, outputting feature information of shape C×H×W. The third branch is the spatial attention branch. The input feature map first undergoes average pooling, followed by a 3×3 convolution and regularization layer to output a feature tensor. Then, a sigmoid activation function is used to calculate the spatial attention weights. Finally, the triple attention module sums the output features from the three attention branches and takes the average, completing the feature aggregation within the module.

[0057] This invention describes an intelligent automotive lighting assistance control method. Ambient light intensity data is acquired through a light sensor installed at the front of the vehicle; a vehicle speed sensor monitors the vehicle's speed in real time; a road condition sensor collects information such as the curvature and slope of the current road; and a driver status sensor monitors the driver's eye movements and fatigue level. A controller module receives and processes the data from these sensors. When the ambient light intensity is below a preset threshold, the controller generates a command to turn on the headlights; when the vehicle speed exceeds a certain value and the road curvature is significant, the controller generates a command to switch to high beams. Furthermore, when driver fatigue is detected, the controller can trigger the activation of interior reading lights to remind the driver to rest. Upon receiving the commands from the controller, the lighting execution module performs the corresponding lighting adjustments to ensure the appropriate use of vehicle lights in different driving environments.

[0058] This implementation integrates a multi-functional intelligent lighting control system that adjusts the lights according to the external environment and vehicle status. It also integrates with the navigation system; when the navigation system detects a tunnel ahead, it turns on the headlights in advance; when the road ahead is narrow or there are obstacles, it automatically switches to low beams. In summary, this invention provides an intelligent automotive lighting assistance control method and system. This system automatically adjusts vehicle lighting by collecting ambient light intensity, vehicle speed, road condition information, and driver status data, including turning headlights on and off, switching between high and low beams, and controlling auxiliary lights. This significantly improves driving safety and comfort, and facilitates innovation in automotive intelligent lighting models and technological frameworks.

[0059] Obviously, the specific implementation of this invention is not limited to the above-described methods. Any non-substantial improvements made using the inventive concept and technical solution of this invention are within the protection scope of this invention.

Claims

1. A vehicle intelligent lighting assistance control system, characterized in that: It includes an environmental sensing module, a system control module, and a system execution module; wherein the environmental sensing module is used to collect vehicle driving status data and driving environment data, and its output is connected to the system control module; the system control module is used to generate lighting control signals based on vehicle driving status data and driving environment data, and its output is connected to the system execution module; the system execution module receives the lighting control signals and executes them to control the vehicle lights; The auxiliary control of vehicle lights includes: after the headlight auxiliary control function is activated, the vehicle's headlights are turned on and are in the initial angle position of the headlight illumination angle; The system collects vehicle speed and steering wheel angle in real time, and adjusts the headlight beam angle based on the speed and steering wheel angle. It also controls the angle between the headlight beam direction and the horizontal direction based on the speed, where the angle w between the headlight beam direction and the horizontal direction gradually decreases to a set threshold as the vehicle speed increases. Simultaneously, the headlight brightness is increased while the headlight beam angle w is reduced.

2. The automotive intelligent lighting assist control system as described in claim 1, characterized in that: The environmental sensing module includes an on-board sensor, which collects vehicle driving data and environmental data of the vehicle's location.

3. The automotive intelligent lighting assist control system as described in claim 2, characterized in that: The system control module includes a central processing unit and an algorithm unit. It receives data transmitted from the sensing module, analyzes and processes the data based on a preset algorithm, and generates corresponding control signals.

4. The automotive intelligent lighting assist control system as described in claim 3, characterized in that: The system execution module includes a vehicle lighting system and an interior lighting system; the vehicle lighting system and the interior lighting system receive control signals and execute them to control the exterior lights and / or the interior lighting system.

5. A vehicle intelligent lighting assist control system as described in any one of claims 1-4, characterized in that: The control system also includes a system communication module, which is connected to the system control module. The system communication module is used to enable communication between vehicles or between vehicles and traffic equipment to assist in vehicle lighting control.

6. A vehicle intelligent lighting assist control system as described in any one of claims 1-4, characterized in that: The system control module contains an image processing algorithm that runs in its algorithm unit. This algorithm analyzes and identifies the vehicle's environment using image data of the vehicle's surroundings. The image processing algorithm includes a neural network model for image recognition and classification.

7. A method for intelligent automotive lighting assistance control, characterized in that: include: The system collects ambient light intensity data while the vehicle is in motion. When the ambient light intensity value is lower than a set threshold, the system control module is activated. The system control module collects environmental data and vehicle status data in real time and generates corresponding control signals to the system execution module. The system execution module executes the control signals and implements auxiliary control of the lights. The auxiliary control of vehicle lights includes: after the headlight auxiliary control function is activated, the vehicle's headlights are turned on and are in the initial angle position of the headlight illumination angle; The system collects vehicle speed and steering wheel angle in real time, and adjusts the headlight beam angle based on the speed and steering wheel angle. It also controls the angle between the headlight beam direction and the horizontal direction based on the speed, where the angle w between the headlight beam direction and the horizontal direction gradually decreases to a set threshold as the vehicle speed increases. Simultaneously, the headlight brightness is increased while the headlight beam angle w is reduced.

8. The automotive intelligent lighting auxiliary control method as described in claim 7, characterized in that: The control signals generated by the system control module include headlight control signals and interior lighting control signals, which are sent to the headlight system and interior lighting system respectively to achieve separate control of the interior and exterior lights.

9. A method for intelligent automotive lighting assistance control as described in any one of claims 7-8, characterized in that: After the system control module starts working, it interacts with surrounding vehicles or traffic equipment through the system communication module and controls the vehicle lights based on the information exchanged.

10. A method for intelligent automotive lighting assistance control as described in any one of claims 7-8, characterized in that: During the lighting auxiliary control process, the system control module acquires the adjustment signal entered by the user through the manual switch in real time. After receiving the lighting adjustment signal entered by the user through the manual switch, the system control module executes the user's adjustment signal and holds it for a set time before starting the lighting auxiliary control function of the system control module.

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