Intelligent sensing regulation and control system for car lamp

Abstract

The invention, which belongs to the technical field of the vehicle headlamp, discloses a vehicle lamp intelligent sensing regulation and control system comprising a sensing layer, a decision-making layer and an execution layer. The sensing layer monitors the vehicle attitude in real time through an attitude sensing unit comprising a gyroscope, and obtains the front road information through a map pre-judgment unit; the decision-making layer carries out fusion processing on the multi-source information, and generates an optimal vehicle lamp adjusting instruction by means of algorithms such as gradient compensation, steering follow-up and map cooperation; and the execution layer drives the lamp cap to realize horizontal and pitching rotation. According to the system, self-adaption and high-precision regulation and control of the irradiation angle of the vehicle lamp are achieved through cooperation of multiple modules, and the night driving safety and the driving comfort are effectively improved. According to the intelligent sensing regulation and control system for the vehicle lamp, the technical problem of how to improve the safety and comfort of a vehicle headlamp system is solved.

Description

Technical Field

[0001] This invention relates to the technical field of vehicle headlights, and in particular to an intelligent sensing and control system for vehicle lights. Background Technology

[0002] The headlight system is a crucial control system in automotive technology, inextricably linked to the development of automobiles. With the increasing prevalence of automobiles and continuous advancements in research and development, the demands on automotive lighting control systems are rising to meet the complex and ever-changing driving environments, thus driving comprehensive technological breakthroughs in this system. While existing headlight systems provide basic road illumination during vehicle operation, they still have significant shortcomings in environments with poor lighting conditions or complex road situations. In terms of upgrading lighting functions, to meet the growing demands for safe driving, the diversification and intelligentization of automotive headlights will become an inevitable trend in the development of automotive headlights.

[0003] To address the blind spots experienced by drivers using traditional headlight systems in complex road conditions, and the glare caused to other drivers by the driver's failure to notice the switching between high and low beams, Adaptive Front-Lighting System (AFS) and Adaptive Driving Beam (ADB) were developed. These two systems play a crucial role in intelligent vehicle lighting control systems. AFS and ADB systems automatically adjust the beam pattern of the LED headlights based on the surrounding environment and the vehicle's own status using various sensors and algorithms. This significantly improves the nighttime driving lighting environment, providing optimal illumination and playing a vital role in reducing traffic accidents.

[0004] Based on this, Chinese patent document CN103253181B discloses a transmission mechanism for an automotive headlight AFS module, which includes a lighting unit, a U-shaped rotating frame, a horizontal rotating motor, and a vertical dimming motor. A bearing is fixed to the U-end of the rotating frame. A first vertical rotating shaft and a second vertical rotating shaft are respectively mounted on the bearing and fixed to the left and right ends of the baffle assembly of the lighting unit. The output shaft of the horizontal rotating motor is connected to the bottom of the rotating frame, and a support shaft is connected to the housing via a support bearing. The top of the support shaft is connected to the housing of the horizontal rotating motor. The horizontal rotating shaft is rotatably connected to the top of the baffle assembly of the lighting unit. One end of the vertical power rod is connected to the horizontal rotating shaft, and the other end is connected to the output end of the vertical dimming motor. A second manual dimming mechanism mounted on the housing is connected to the vertical dimming motor. This transmission mechanism for an automotive headlight AFS module has the advantage of simple structure, and while satisfying the headlight rotation dimming function, it can also improve the vibration resistance of the lamp.

[0005] However, existing headlight adaptive lighting solutions still suffer from technical shortcomings in safety and comfort. Specifically, some basic AFS (Adaptive Front-lighting) systems calculate vehicle turning intentions solely based on steering wheel angle signals and vehicle speed. This calculation method suffers from latency and cannot detect attitude changes caused by body roll or road bumps, such as encountering potholes while turning. Moreover, many existing AFS systems focus more on horizontal steering, with pitch adjustment often being a simple, linear compensation rather than a non-linear intelligent adjustment based on precise slope recognition. Furthermore, a complete intelligent headlight system combining AFS and ADB (Adaptive Driving Depth) systems might include a forward-facing camera, wheel speed sensors, steering wheel angle sensors, GPS / Navigation, and a complex image processing chip (for ADB). This system is costly and complex, making it difficult to promote and deploy in mainstream vehicles. Furthermore, in some camera-dependent ADB systems, the performance of the cameras deteriorates drastically or even fails in adverse weather conditions such as heavy rain, fog, strong light interference, or when the lenses are obstructed by dirt, causing the ADB function to shut down. All of these defects affect driving safety and user comfort. Summary of the Invention

[0006] Therefore, it is necessary to provide a vehicle headlight intelligent sensing and control system to address the technical issues of how to improve the safety and comfort of vehicle headlight systems.

[0007] A vehicle lighting intelligent sensing and control system includes: a sensing layer, a decision-making layer, and an execution layer; The perception layer is used to acquire vehicle attitude information and road information ahead in real time. The decision layer is used to fuse and process the information acquired by the perception layer and generate headlight adjustment commands based on preset strategies. The execution layer is used to receive headlight adjustment commands from the decision layer and drive the headlights to perform corresponding attitude adjustments. The perception layer includes at least a gyroscope module for detecting the dynamic attitude of the vehicle, the decision layer includes at least a processing unit for realizing multi-source information fusion and scene recognition, and the execution layer includes at least a drive unit for realizing the movement of the lamp head.

[0008] Specifically, in one embodiment, the perception layer includes an attitude perception unit and a map prediction unit. The attitude perception unit has a gyroscope module, which monitors the dynamic attitude changes of the vehicle in real time and outputs the vehicle's real-time three-axis attitude data. The map prediction unit obtains the geographic information data of the road ahead from the digital map through the vehicle network system and outputs prediction information. The decision-making layer includes a signal processing and fusion unit and a scene recognition and decision-making unit. The signal processing and fusion unit receives and filters the raw data from the attitude perception unit, and performs spatiotemporal alignment and fusion with the future road condition information provided by the map prediction unit to generate a forward-looking vehicle environment state dataset. Based on the fused vehicle environment state dataset, the scene recognition and decision-making unit calls the built-in preset intelligent algorithm to identify the current driving scenario and generate the optimal headlight adjustment command according to the preset strategy. Among them, the preset intelligent algorithms include slope compensation algorithm, steering follow-up algorithm and map cooperation algorithm; The execution layer includes a lamp head drive control unit and an actuator; the lamp head drive control unit receives control commands from the decision layer and converts them into control signals for the drive motor, and the actuator drives the lamp head to achieve horizontal rotation or vertical pitching based on the received control signals.

[0009] Specifically, the gyroscope module can be a built-in gyroscope module or an external gyroscope module; the built-in gyroscope module is integrated inside the vehicle's headlights; the external gyroscope module is installed in the vehicle's steering wheel, center console, windshield, or wheel hub.

[0010] Specifically, when the vehicle enters a left or right turn, the built-in gyroscope module detects the horizontal rotation amplitude of the vehicle's direction of travel and adjusts the illumination direction of the headlight accordingly; when the vehicle is going uphill or downhill, the built-in gyroscope module detects the change in the vehicle's pitch angle and then adjusts the vertical rotation angle of the headlight so that the headlight can adapt to different road conditions.

[0011] Specifically, the external gyroscope module has a built-in battery and wireless communication module; it can operate independently and transmit data to the decision-making level via the wireless communication module.

[0012] Furthermore, the steps of the slope compensation algorithm are as follows: Step 1: Data Acquisition and Preprocessing The system collects raw data of the vehicle's pitch angle and pitch velocity in real time through the attitude sensing unit; the raw data is first passed through a dynamic low-pass filter to filter out noise interference caused by high-frequency road bumps; Step 2: Determining the Effective Slope Status: The algorithm continuously monitors the filtered pitch angular velocity value; it determines the current vehicle motion state using a preset dynamic threshold. If the angular velocity is lower than or equal to the threshold, it indicates that the vehicle is in a stable slope change, which is determined to be a valid slope state, and the program continues to execute step three; If the angular velocity is higher than the threshold, it indicates that the vehicle is experiencing instantaneous turbulence, which is determined to be invalid vibration; the algorithm will maintain the current angle of the headlights, suppress the output, and avoid unnecessary frequent shaking of the lights; Step 3: Query the slope-angle control mapping table: Once a valid slope is determined, the algorithm compares the filtered real-time pitch angle with the slope-angle mapping table preset in the system. The algorithm traverses the mapping table and locks the corresponding target compensation angle when it finds the interval in which the current pitch angle falls. Step 4: Output target control instructions: After querying the mapping table, the algorithm outputs a precise target angle command for the lamp head's pitch axis; this command is then weighted and fused with the predictive information provided by the map collaboration algorithm to finally generate a smooth and continuous optimal control command. Step 5: Instruction Execution and Closed-Loop Feedback The lamp head drive control unit receives the final command and drives the stepper motor to rotate the lamp head to the target angle; the feedback element built into the motor returns the actual angle of the lamp head to the decision-making level in real time, forming a closed-loop control system to ensure execution accuracy and prevent error accumulation; Step Six: Continuous Monitoring and Cycling The above process is executed cyclically in milliseconds during vehicle operation.

[0013] Furthermore, in the slope-angle control mapping table of step three of the slope compensation algorithm mentioned above, the slope and the lamp head rotation angle have a piecewise linear relationship: when the slope does not exceed 36%, a slope increment of 5%-15% is defined as an interval, and when the vehicle ascends one interval, the lamp head rotation angle increases by 1 degree; while when the slope exceeds 36%, a slope increment of 36%-70% is defined as an interval, and when the vehicle ascends one interval, the lamp head rotation angle increases by 1 degree.

[0014] Furthermore, in the slope-angle control mapping table of step three of the slope compensation algorithm above, let the slope be α and the rotation angle be β. When 0°≤α≤35°, β=floor(α / 7)+2, where the units of α and β are degrees.

[0015] Furthermore, the steps of the steering follow-up algorithm are as follows: Step 1: Multi-source signal acquisition and synchronization: The system collects data from different sensors in real time: Vehicle dynamic signals: The vehicle's yaw rate is obtained from the attitude sensing unit; Driver intention signals: Steering wheel angle and vehicle speed are obtained via the vehicle's CAN bus; Assistive intent signal: Collects turn signal signals as direct evidence of the driver's lane-changing intention; Step 2: Signal Filtering and Preprocessing The collected raw signals are filtered to eliminate high-frequency vehicle vibration and signal noise during high-speed driving; Step 3: Driving Scene Recognition The algorithm analyzes and processes the multi-source signals to make intelligent judgments on the current driving scenario, and calls the corresponding preset differentiated control strategies based on the determined driving scenario. Step 5: Calculation and output of target deflection angle: Based on the selected strategy, the horizontal deflection angle of the target lamp head is calculated; at the same time, the calculation result is fused with the prediction information provided by the map collaboration algorithm, and the final optimal command is output to the lamp head drive control unit. Step Six: Command Execution and Closed-Loop Control The lamp head drive control unit receives the command and drives the horizontal steering motor to rotate to the target angle; Step 7: Continuous Cyclic Monitoring: The above process runs in a loop with a cycle of milliseconds.

[0016] Furthermore, the steps of the map collaboration algorithm are as follows: Step 1: Obtain geographic location and map data: The system obtains the vehicle's real-time precise location, heading, and speed through the positioning module; at the same time, it requests and receives prediction information data packets of the road within a preset distance ahead of the vehicle from the high-precision digital map service through the vehicle-mounted network system. Step 2: Extracting and analyzing road feature information: The algorithm parses the received map data packets and extracts key road feature information; Step 3: Calculate spatial distance and time estimate Based on the vehicle's current position, heading, and the coordinates of feature points on the road ahead, the algorithm calculates the straight-line distance or path distance from the vehicle to the preset feature point; combined with the current real-time vehicle speed, it estimates the time it will take for the vehicle to reach that point. Step 4: Generate the predicted target angle: Based on the extracted road features, calculate the ideal prediction angle for the light head: For curves, a target horizontal deflection angle is obtained based on the radius of curvature and vehicle speed using a preset lookup table or calculation formula. For the slope, a target pitch compensation angle is derived based on the slope and the rate of change of the slope. Step 5: Generate a smooth control command sequence Based on the estimated time calculated in step three, a smooth transition trajectory from the current angle of the lamp head to the predicted target angle is generated. Step Six: Arbitration of Fusion with Real-Time Sensor Data The generated smooth prediction command is sent to the fusion unit of the decision layer and weighted and fused with the command calculated by the slope compensation algorithm and the steering follow-up algorithm based on real-time gyroscope data. Weight allocation: The system dynamically allocates weights based on the distance to the target features; Conflict arbitration: When map prediction information conflicts with real-time sensor information, arbitration is conducted according to a preset priority arbitration mechanism; Step 7: Output final instructions and fault degradation The final fused command is sent to the lamp head drive control unit for execution; throughout the process, the system continuously monitors the validity of the map data; once the GPS signal is lost or the map data is interrupted, the algorithm will automatically and seamlessly degrade to pure sensor mode to ensure that the most basic adaptive lighting function is always available; Step 8: Repeated execution: The above process continues to repeat as the vehicle's position changes.

[0017] In summary, the present invention provides an intelligent vehicle lighting perception and control system, comprising a perception layer, a decision-making layer, and an execution layer. The perception layer includes an attitude perception unit and a map prediction unit. The attitude perception unit has a gyroscope module and monitors the dynamic attitude changes of the vehicle in real time, outputting real-time three-axis attitude data. The map prediction unit acquires geographic information data of the road ahead from a digital map through a vehicle network system and outputs prediction information. The decision-making layer includes a signal processing and fusion unit and a scene recognition and decision-making unit. The signal processing and fusion unit receives and filters the raw data from the attitude perception unit and compares it with the future road conditions provided by the map prediction unit. Information is spatiotemporally aligned and fused to generate a forward-looking vehicle environment state dataset. Based on the fused dataset, the scene recognition and decision-making unit invokes built-in preset intelligent algorithms to identify the current driving scenario and generate optimal headlight adjustment commands according to preset strategies. These preset intelligent algorithms include slope compensation, steering follow-up, and map collaboration algorithms. The execution layer includes a headlight drive control unit and an actuator. The headlight drive control unit receives control commands from the decision layer and converts them into control signals for the drive motor. The actuator drives the headlight to achieve horizontal rotation or vertical pitch movement based on the received control signals. This invention proposes a modular, low-coupling intelligent headlight control system. It uses a posture perception unit and a map prediction unit to form the system's perceptual neural network. The signal processing and fusion unit and the scene recognition and decision-making unit act as the brain for intelligent decision-making, while the headlight drive control unit and actuator act as the limbs to execute precise actions. All units are organically connected through a vehicle network bus, working collaboratively to achieve adaptive, high-precision, and high-reliability intelligent control of the headlight illumination angle, significantly improving nighttime driving safety and driving experience. Therefore, the intelligent sensing and control system for vehicle lights of this invention solves the technical problem of how to improve the safety and comfort of vehicle headlight systems. Attached Figure Description Figure 1 This is a schematic diagram of the system framework of an intelligent vehicle lighting sensing and control system according to the present invention; Figure 2 This is a flowchart of the slope compensation algorithm for an intelligent vehicle lighting sensing and control system according to the present invention. Figure 3 This is a flowchart of the steering follow-up algorithm of the intelligent vehicle lighting sensing and control system of the present invention; Figure 4 This is a flowchart of the map collaboration algorithm for a vehicle headlight intelligent perception and control system according to the present invention. Detailed Implementation

[0018] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0019] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0020] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0021] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components, unless otherwise explicitly limited. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0022] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can mean that the first feature is in direct contact with the second feature, or that the first feature is in indirect contact with the second feature through an intermediate medium. Furthermore, "above," "over," and "on top" of the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature.

[0023] It should be noted that when an element is referred to as being "fixed to" or "set on" another element, it can be directly on the other element or there may be an intervening element. When an element is considered to be "connected to" another element, it can be directly connected to the other element or there may be an intervening element. The terms "vertical," "horizontal," "upper," "lower," "left," "right," and similar expressions used herein are for illustrative purposes only and do not represent the only possible implementation.

[0024] Please see Figure 1 This invention discloses an intelligent vehicle lighting perception and control system, comprising: a perception layer, a decision layer, and an execution layer; the perception layer includes an attitude perception unit and a map prediction unit, the attitude perception unit having a gyroscope module, the attitude perception unit monitors the dynamic attitude changes of the vehicle in real time and outputs real-time three-axis attitude data of the vehicle; the map prediction unit acquires the geographic information data of the road ahead from a digital map through a vehicle network system and outputs prediction information; the decision layer includes a signal processing and fusion unit and a scene recognition and decision unit; the signal processing and fusion unit receives and filters the raw data from the attitude perception unit and combines it with the future road condition information provided by the map prediction unit. Spatiotemporal alignment and fusion are performed to generate a forward-looking vehicle environment state dataset. Based on the fused vehicle environment state dataset, the scene recognition and decision-making unit calls the built-in preset intelligent algorithms to identify the current driving scenario and generate the optimal headlight adjustment command according to the preset strategy. The preset intelligent algorithms include slope compensation algorithm, steering follow-up algorithm, and map cooperation algorithm. The execution layer includes a headlight drive control unit and an actuator. The headlight drive control unit receives the control commands from the decision layer and converts them into control signals for the drive motor. The actuator drives the headlight to achieve horizontal rotation or vertical pitch movement according to the received control signals.

[0025] Specifically, the aforementioned attitude sensing unit functions to monitor the vehicle's dynamic attitude changes in real time. For example, the monitored attitudes include pitch, roll, yaw angle, and angular velocity. The attitude sensing unit has a gyroscope module, which can be either an internal or external gyroscope module. When it is an internal gyroscope module, it can be integrated into the headlight assembly to directly measure the relative motion between the headlight and the vehicle body. When it is an external gyroscope module, it can be independently packaged. Furthermore, the external gyroscope module incorporates a battery and a wireless communication module, such as a Bluetooth communication module. This type of external gyroscope module can be installed on the vehicle's steering wheel, center console, or wheel hub for redundancy or to improve measurement accuracy. During the monitoring period, the attitude sensing unit can output real-time three-axis attitude data of the vehicle.

[0026] Specifically, the function of the map prediction unit is to obtain geographic information data of the road ahead from the digital map through the vehicle network system, such as a vehicle network system with GPS+4G / 5G modules; and output prediction information, such as the curvature (curve), slope, slope change rate, tunnel entrance, etc. of the road ahead.

[0027] Specifically, the aforementioned signal processing and fusion unit functions as follows: it receives and filters the raw data from the attitude perception unit to eliminate high-frequency noise, such as high-frequency jitter caused by potholes in the road surface. Simultaneously, it performs spatiotemporal alignment and fusion with future road condition information provided by the map prediction unit, thereby generating a reliable and forward-looking vehicle environment state dataset.

[0028] Specifically, the scene recognition and decision-making unit functions as follows: based on the fused vehicle environment state dataset, it calls its built-in intelligent algorithm model to identify the current driving scenario and generate the optimal headlight adjustment command according to a preset strategy.

[0029] More specifically, the aforementioned intelligent algorithm models include slope compensation algorithm, steering follow-up algorithm, and map collaboration algorithm.

[0030] The slope compensation algorithm can determine the slope based on the pitch angle and query a preset slope-angle mapping table, such as 0-3° down by 2°, 3-8° down by 3°, etc., and then output the vertical adjustment amount; this algorithm has the function of identifying potholes and uneven road surfaces, so as to suppress invalid adjustments.

[0031] The steering follow-up algorithm can determine whether to change lanes, turn, or make a U-turn based on the yaw rate and steering angle, and output different proportions of horizontal adjustment.

[0032] The map collaboration algorithm can adjust the headlight angle in advance based on predictive information, such as starting to slowly deflect before entering a curve, and making the adjustment process smoother and more in line with driving expectations.

[0033] Furthermore, the lamp head drive control unit in the execution layer can receive control commands from the decision layer, such as "turn left 5° horizontally" or "turn down 3° pitch", and convert them into control signals for the drive motor, such as the number of PWM pulses. The lamp head drive control unit features closed-loop feedback, and can detect the actual rotation angle of the motor through components such as encoders to ensure precise execution.

[0034] Specifically, the actuator consists of two stepper motors or servo motors and their mechanical structures, which are responsible for driving the lamp head to achieve horizontal rotation (A-axis) and vertical pitch (B-axis) movement.

[0035] Furthermore, the intelligent vehicle lighting sensing and control system of the present invention can also be provided with an interaction and configuration layer, which includes a system calibration and configuration unit and a human-machine interaction unit. The system calibration and configuration unit is used at the end of the production line or during after-sales maintenance to calibrate and configure the system's initial zero position, maximum deflection angle, algorithm parameters, etc. The human-machine interaction unit allows the driver to set the system switch, adjust the sensitivity, or view the current system status through the central control screen.

[0036] Specifically, the data flow control logic of the intelligent vehicle lighting perception and control system of the present invention is as follows: the attitude perception unit and the map prediction unit send the collected raw data to the decision layer via the CAN bus, i.e., the vehicle's main network or wireless link (for external modules); the signal processing and fusion unit in the decision layer cleans and fuses the data; the scene recognition and decision unit makes a decision based on the processed data and generates control commands; the control commands are sent to the lamp head drive control unit in the execution layer via the LIN bus or a direct wire connection; the lamp head drive control unit drives the actuator to complete the action.

[0037] Specifically, the control flow logic of the intelligent vehicle lighting perception and control system of this invention is as follows: the control logic of the entire system is centered on the decision-making layer, which is a centralized and intelligent "brain". The perception layer is the "eyes" of the system, responsible for perception but not decision-making. The execution layer is the "hands and feet" of the system, strictly obeying the instructions of the decision-making layer. The interaction and configuration layer serves as the "parameter setting interface" of the system, initializing the parameters of the core algorithm units during system startup or maintenance.

[0038] Specifically, in the intelligent vehicle lighting sensing and control system of the present invention, the entire system is powered by the vehicle's battery, and a power management chip provides a stable voltage to each unit; when the attitude sensing unit is an external gyroscope module, the vehicle's battery can also charge the battery of the external gyroscope module.

[0039] In summary, the intelligent vehicle headlight perception and control system of this invention provides a hierarchical, modular, and low-coupling intelligent vehicle headlight control system. It utilizes a posture perception unit and a map prediction unit to form the system's perception neural network, with a signal processing and fusion unit and a scene recognition and decision-making unit acting as the brain for intelligent decision-making, and the headlight drive control unit and actuators acting as the limbs to execute precise actions. All units are organically connected through the vehicle network bus, working collaboratively to ultimately achieve adaptive, high-precision, and high-reliability intelligent control of the headlight illumination angle, significantly improving nighttime driving safety and the driving experience.

[0040] Therefore, this invention proposes an intelligent headlight sensing and control system based on vehicle dynamic behavior perception. By detecting and identifying the dynamic state of the vehicle, it realizes intelligent adjustment of the headlight illumination angle to optimize driving safety and comfort.

[0041] Specifically, during vehicle driving, the intelligent vehicle headlight sensing and control system of this invention monitors vehicle status changes in real time through a built-in or external three-axis gyroscope. For example, the built-in gyroscope module can be integrated inside the vehicle's headlights, which can identify changes in vehicle attitude through the relative movement of the headlights and the vehicle body. For instance, when the vehicle enters a left or right turn, the built-in gyroscope module can adjust the illumination direction of the headlights accordingly by detecting the horizontal rotation amplitude of the vehicle's direction of travel. When the vehicle is going uphill or downhill, the built-in gyroscope module will detect changes in the vehicle's pitch angle and then adjust the vertical rotation angle of the headlights to adapt the headlights to different road conditions.

[0042] Furthermore, the external gyroscope module can be installed in the vehicle's steering wheel, center console, windshield, or wheel hub. It operates independently through its built-in battery and wireless communication module and transmits data to the vehicle's main control module, i.e., the aforementioned decision-making layer, via Bluetooth or other wireless modules, thereby further improving the accuracy of vehicle status monitoring.

[0043] For further details, please refer to Figure 2 In the intelligent vehicle lighting sensing and control system of the present invention, the specific implementation steps of a slope compensation algorithm are as follows: Step 1: Data Acquisition and Preprocessing The system uses an attitude sensing unit (built-in or external three-axis gyroscope) to collect raw data of the vehicle's pitch angle and pitch velocity in real time; the raw data is first passed through a dynamic low-pass filter to filter out noise interference caused by high-frequency road bumps, ensuring that the signal processed in subsequent steps is stable and reliable. Step 2: Determining the Effective Slope Status: The algorithm continuously monitors the filtered pitch velocity value; by setting a preset dynamic threshold, the value of which can be adjusted according to the vehicle speed, the current vehicle motion state is determined. If the angular velocity is lower than or equal to the threshold, it indicates that the vehicle is in a stable slope change, such as continuous uphill / downhill, which is determined to be a valid slope state, and the program continues to execute the next step of querying the mapping table; If the angular velocity is higher than the threshold, it indicates that the vehicle is experiencing instantaneous bumps, such as driving over potholes, which is considered invalid vibration; the algorithm will maintain the current angle of the headlights, suppress the output, and avoid unnecessary frequent shaking of the lights; Step 3: Query the slope-angle control mapping table: Once a valid slope is determined, the algorithm compares the filtered real-time pitch angle with a slope-angle mapping table preset within the system. This mapping table defines the precise correspondence between the pitch angle or equivalent slope percentage range and the lamp head compensation angle. One implementation method is as follows: Pitch angle 0° to 3° (slope 0% to 5%): the lamp head rotates downwards by 2 degrees; Pitch angle 3° to 8° (slope 5% to 15%): the lamp head is rotated downwards by 3 degrees; Pitch angle 8° to 20° (slope 15% to 36%): lamp head rotates downwards by 4 degrees; Pitch angle 20° to 28° (slope 36% to 70%): Turn the lamp head downwards by 5 degrees; Pitch angle 28° to 35° (slope 36% to 70%): lamp head rotates downwards by 6 degrees; The algorithm traverses the mapping table and locks the corresponding target compensation angle when it finds the interval in which the current pitch angle falls. Step 4: Output target control instructions: After querying the mapping table, the algorithm outputs a precise target angle command for the lamp head's pitch axis (B-axis). This command is usually then weighted and fused with the predictive information provided by the map collaboration algorithm. If there is a steeper slope ahead, it is fine-tuned in advance to finally generate a smooth and continuous optimal control command. Step 5: Instruction Execution and Closed-Loop Feedback The lamp head drive control unit receives the final command and drives the stepper motor to rotate the lamp head to the target angle; the encoder and other feedback components built into the motor will return the actual angle of the lamp head to the main control module in real time, forming a closed-loop control system to ensure execution accuracy and prevent error accumulation. Step Six: Continuous Monitoring and Cycling The above process is executed continuously in a loop during vehicle operation, typically at millisecond intervals, thereby achieving real-time, accurate, and smooth adaptive lighting compensation for all slope changes.

[0044] For further details, please refer to Figure 3 In the intelligent vehicle lighting sensing and control system of the present invention, the specific steps of an implementation of a steering follow-up algorithm are as follows: Step 1: Multi-source signal acquisition and synchronization: The system collects data from multiple sensors in real time: Vehicle dynamic signals: The yaw rate of the vehicle is obtained from the attitude sensing unit, i.e., the three-axis gyroscope, which is the angular velocity of the vehicle rotating around the vertical axis. Driver intention signals: Steering wheel angle and vehicle speed are obtained via the vehicle's CAN bus; Assistive intent signal: Collects turn signal signals as direct evidence of the driver's lane-changing intention; Step 2: Signal Filtering and Preprocessing The raw signals collected, especially the yaw rate, are filtered to eliminate high-frequency vehicle vibration and signal noise during high-speed driving, ensuring the stability and reliability of the data and providing a clean data foundation for subsequent scene recognition. Step 3: Driving Scene Recognition The algorithm comprehensively analyzes and processes multi-source signals to make intelligent judgments about the current driving scenario. Minor steering, i.e. lane changing scenarios: characterized by a small steering wheel angle, low yaw rate, and usually accompanied by turn signal signals; Turning / U-turn scenarios: Characterized by large steering wheel angles and consistently high yaw rates; Straight driving / returning to center scenario: Characterized by steering wheel angle close to zero and yaw rate extremely low; Step 4: Scenario-based control strategy selection: Based on the identified driving scenario, a preset differentiated control strategy is invoked: For lane-changing scenarios: a strategy of low gain and short delay is adopted; the headlight deflection angle is small, the response is fast but gentle, and unnecessary swaying of the lights is avoided due to slight adjustments of the steering wheel, thus improving comfort; For turning / U-turn scenarios: a high-gain, speed-sensitive adjustment strategy is adopted; the headlight deflection angle is more proportional to the steering wheel angle or yaw rate to ensure sufficient illumination of the inside of the curve; at the same time, the deflection amplitude and response speed are dynamically fine-tuned according to the vehicle speed, such as being more sensitive at low speeds and more stable at high speeds. For straight driving / returning to center scenarios: control the headlights to smoothly and slowly return to the zero position, i.e., the default driving position; Step 5: Calculation and output of target deflection angle: Based on the selected strategy, the horizontal deflection angle of the target lamp head, i.e. the rotation angle of the A-axis, is calculated. The calculation result is usually fused with the predictive information provided by the map collaboration algorithm, such as the curvature of the road ahead, so as to achieve earlier and smoother lighting of curves. The final optimal command is output to the lamp head drive control unit. Step Six: Command Execution and Closed-Loop Control The lamp head drive control unit receives commands and drives the horizontal steering motor, i.e., the A-axis motor, to rotate to the target angle; feedback elements such as encoders form a closed-loop control to ensure execution accuracy. Step 7: Continuous Cyclic Monitoring: The above process runs in a loop with an extremely short cycle, such as a millisecond cycle, to achieve real-time and continuous tracking and response to the vehicle's steering status, ensuring that optimal side lighting can be provided in various dynamic driving scenarios. For further details, please refer to Figure 4In the intelligent vehicle lighting sensing and control system of the present invention, the specific implementation steps of a map cooperative algorithm are as follows: Step 1: Obtain geographic location and map data: The system obtains the vehicle's real-time precise location, heading, and speed through positioning modules such as GPS / BeiDou; at the same time, it requests and receives predictive information data packets of the road ahead of the vehicle at a certain distance, such as 300-500 meters, from high-precision digital map services through in-vehicle network systems such as 4G / 5GT-Box. Step 2: Extracting and analyzing road feature information: The algorithm parses the received map data packets and extracts key road feature information, mainly including: Road curvature / curve radius: used to predict horizontal turning, i.e. the angle required for the A-axis; Longitudinal slope and slope change rate: used to predict pitch adjustment, i.e. the angle required for B-axis; Special road types: such as tunnels, ramps, intersections, and other locations that may trigger specific lighting patterns; Step 3: Calculate spatial distance and time estimate Based on the vehicle's current position, heading, and the coordinates of a feature point on the road ahead, the algorithm calculates the straight-line distance or the distance along the path from the feature point to the vehicle; combined with the current real-time vehicle speed, it estimates the time it will take for the vehicle to reach the point, such as time = distance / speed. Step 4: Generate the predicted target angle: Based on the extracted road features, calculate the ideal prediction angle for the light head: For curves, a target horizontal deflection angle is obtained based on the radius of curvature and vehicle speed using a preset lookup table or calculation formula. For the slope, a target pitch compensation angle is derived based on the slope and the rate of change of the slope. Step 5: Generate a smooth control command sequence The algorithm prevents the headlight from suddenly jumping to the predicted angle; based on the estimated time calculated in step three, it generates a smooth transition trajectory from the current angle of the headlight to the predicted target angle, for example, by using an S-curve acceleration / deceleration algorithm; this ensures that the headlight rotation is advance, slow and linear, resulting in a very natural driving experience without any abruptness. Step Six: Arbitration of Fusion with Real-Time Sensor Data The generated smooth prediction command is not executed alone, but is sent to the fusion unit of the decision layer and weighted and fused with the command calculated by the slope compensation algorithm and the steering follow-up algorithm based on real-time gyroscope data. Weighting: The system dynamically assigns weights based on the distance to the target features; for example, when the distance is far, the predictive command has a higher weight; when the distance is close, the weight of the real-time sensor command gradually increases to ensure the accuracy of the final execution. Conflict arbitration: When map prediction information conflicts with real-time sensor information, for example, the map shows a curve but the sensor does not detect the turn, the system usually has a priority arbitration mechanism. The reliability of real-time sensors usually has a higher priority, and logs are recorded for analysis. Step 7: Output final instructions and fault degradation The final fused command is sent to the lamp head drive control unit for execution; throughout the process, the system continuously monitors the validity of the map data; once the GPS signal is lost or the map data is interrupted, the algorithm will automatically and seamlessly degrade to pure sensor mode to ensure that the most basic adaptive lighting function is always available; Step 8: Execute repeatedly: The above process continues to cycle as the vehicle's position changes, thereby achieving continuous and forward-looking lighting planning for the entire journey.

[0045] Specifically, compared to traditional AFS systems, the intelligent headlight sensing and control system of this invention boasts extremely high response speed and reliability. Traditional AFS systems calculate vehicle turning intentions solely based on steering wheel angle signals and vehicle speed; this calculation method suffers from latency and cannot detect attitude changes caused by vehicle tilt or road bumps, such as when encountering potholes while turning. In contrast, the intelligent headlight sensing and control system of this invention uses a gyroscope to directly measure the vehicle's physical motion, such as angular velocity and acceleration, resulting in extremely low response latency, typically down to the millisecond level. Whether it's a sudden turn, an emergency lane change, or driving over potholes, this system can react almost in real-time and adjust the headlights accordingly.

[0046] Specifically, compared to traditional AFS systems, this invention's intelligent vehicle headlight sensing and control system features dedicated optimization for vehicle pitch attitude. Existing AFS systems focus more on horizontal steering, and pitch adjustment may be a simple, linear compensation rather than a non-linear intelligent adjustment based on precise slope recognition. However, this invention employs a refined algorithm for different slopes, such as dividing the system into five intervals, with each interval adjusting the angle differently. This control method effectively solves the technical problem of existing solutions where headlights illuminate the sky when going uphill and the headlights illuminate the ground when going downhill.

[0047] Specifically, compared to traditional AFS systems, the intelligent headlight sensing and control system of this invention has advantages such as better cost-effectiveness and lower implementation complexity. Compared to a complete AFS+ADB system: a complete intelligent headlight system may include: a forward-facing camera, wheel speed sensors, a steering wheel angle sensor, GPS / Navigation, and a complex image processing chip; its cost and system complexity are far higher than the gyroscope-based solution proposed in this invention. The sensor cost used in this invention has been significantly reduced, and the system structure is relatively simple, mainly consisting of attitude perception + mechanical execution, making it easier to promote and deploy in mainstream vehicle models.

[0048] Specifically, compared to the traditional AFS system, the intelligent vehicle lighting sensing and control system of this invention has stability in harsh environments. Compared to the ADB system that relies on cameras, the performance of the camera will drop sharply or even fail when the camera is in bad weather, such as heavy rain, fog, strong light interference, or when the lens is blocked by dirt, causing the ADB function to be turned off; while the gyroscope proposed in this invention is a self-motion sensor, which is not affected by external ambient light or weather, such as rain, snow, fog, dust, etc.; as long as the vehicle is moving, this gyroscope can work normally.

[0049] In summary, the present invention provides an intelligent vehicle lighting perception and control system, comprising a perception layer, a decision-making layer, and an execution layer. The perception layer includes an attitude perception unit and a map prediction unit. The attitude perception unit has a gyroscope module and monitors the dynamic attitude changes of the vehicle in real time, outputting real-time three-axis attitude data. The map prediction unit acquires geographic information data of the road ahead from a digital map through a vehicle network system and outputs prediction information. The decision-making layer includes a signal processing and fusion unit and a scene recognition and decision-making unit. The signal processing and fusion unit receives and filters the raw data from the attitude perception unit and compares it with the future road conditions provided by the map prediction unit. Information is spatiotemporally aligned and fused to generate a forward-looking vehicle environment state dataset. Based on the fused dataset, the scene recognition and decision-making unit invokes built-in preset intelligent algorithms to identify the current driving scenario and generate optimal headlight adjustment commands according to preset strategies. These preset intelligent algorithms include slope compensation, steering follow-up, and map collaboration algorithms. The execution layer includes a headlight drive control unit and an actuator. The headlight drive control unit receives control commands from the decision layer and converts them into control signals for the drive motor. The actuator drives the headlight to achieve horizontal rotation or vertical pitch movement based on the received control signals. This invention proposes a modular, low-coupling intelligent headlight control system. It uses a posture perception unit and a map prediction unit to form the system's perceptual neural network. The signal processing and fusion unit and the scene recognition and decision-making unit act as the brain for intelligent decision-making, while the headlight drive control unit and actuator act as the limbs to execute precise actions. All units are organically connected through a vehicle network bus, working collaboratively to achieve adaptive, high-precision, and high-reliability intelligent control of the headlight illumination angle, significantly improving nighttime driving safety and driving experience. Therefore, the intelligent sensing and control system for vehicle lights of this invention solves the technical problem of how to improve the safety and comfort of vehicle headlight systems.

[0050] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0051] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A vehicle lighting intelligent sensing and control system, characterized in that, It includes: The layers are: perception layer, decision-making layer, and execution layer. The perception layer is used to acquire vehicle attitude information and road information ahead in real time. The decision layer is used to fuse and process the information acquired by the perception layer and generate headlight adjustment commands based on preset strategies. The execution layer is used to receive headlight adjustment commands from the decision layer and drive the headlights to perform corresponding attitude adjustments. The perception layer includes at least a gyroscope module for detecting the dynamic attitude of the vehicle, the decision layer includes at least a processing unit for realizing multi-source information fusion and scene recognition, and the execution layer includes at least a drive unit for realizing the movement of the lamp head.

2. The intelligent sensing and control system for vehicle lights according to claim 1, characterized in that: The gyroscope module can be a built-in gyroscope module or an external gyroscope module; the built-in gyroscope module is integrated inside the vehicle's headlights; the external gyroscope module is installed in the vehicle's steering wheel, center console, windshield, or wheel hub.

3. The intelligent sensing and control system for vehicle lights according to claim 2, characterized in that: When the vehicle enters a left or right turn, the built-in gyroscope module detects the horizontal rotation of the vehicle's direction of travel and adjusts the direction of the headlights accordingly. When the vehicle is going uphill or downhill, the built-in gyroscope module detects the changes in the vehicle's pitch angle and adjusts the vertical rotation angle of the headlights accordingly to adapt the headlights to different road conditions.

4. The intelligent sensing and control system for vehicle lights according to claim 2, characterized in that: The external gyroscope module has a built-in battery and wireless communication module; it can operate independently and transmit data to the decision-making level via the wireless communication module.

5. The intelligent sensing and control system for vehicle lights according to claim 1, characterized in that: The steps of the slope compensation algorithm are as follows: Step 1: Data Acquisition and Preprocessing The system collects raw data of the vehicle's pitch angle and pitch velocity in real time through the attitude sensing unit; the raw data is first passed through a dynamic low-pass filter to filter out noise interference caused by high-frequency road bumps; Step 2: Determining the Effective Slope Status: The algorithm continuously monitors the filtered pitch angular velocity value; it determines the current vehicle motion state using a preset dynamic threshold. If the angular velocity is lower than or equal to the threshold, it indicates that the vehicle is in a stable slope change, which is determined to be a valid slope state, and the program continues to execute step three; If the angular velocity is higher than the threshold, it indicates that the vehicle is experiencing instantaneous turbulence, which is determined to be invalid vibration; the algorithm will maintain the current angle of the headlights, suppress the output, and avoid unnecessary frequent shaking of the lights; Step 3: Query the slope-angle control mapping table: Once a valid slope is determined, the algorithm compares the filtered real-time pitch angle with the slope-angle mapping table preset in the system. The algorithm traverses the mapping table and locks the corresponding target compensation angle when it finds the interval in which the current pitch angle falls. Step 4: Output target control instructions: After querying the mapping table, the algorithm outputs a precise target angle command for the lamp head's pitch axis; this command is then weighted and fused with the predictive information provided by the map collaboration algorithm to finally generate a smooth and continuous optimal control command. Step 5: Instruction Execution and Closed-Loop Feedback The lamp head drive control unit receives the final command and drives the stepper motor to rotate the lamp head to the target angle; the feedback element built into the motor returns the actual angle of the lamp head to the decision-making level in real time, forming a closed-loop control system to ensure execution accuracy and prevent error accumulation; Step Six: Continuous Monitoring and Cycling The above process is executed cyclically in milliseconds during vehicle operation.

6. The intelligent sensing and control system for vehicle lights according to claim 5, characterized in that: In the slope-angle control mapping table in step three, the slope and the lamp head rotation angle have a piecewise linear relationship: when the slope does not exceed 36%, a slope increment of 5%-15% is defined as an interval, and when the vehicle rises one interval, the lamp head rotation angle increases by 1 degree; while when the slope exceeds 36%, a slope increment of 36%-70% is defined as an interval, and when the vehicle rises one interval, the lamp head rotation angle increases by 1 degree.

7. The intelligent sensing and control system for vehicle lights according to claim 5, characterized in that: In the slope-angle control mapping table in step three, let the slope be α and the rotation angle be β. When 0°≤α≤35°, β=floor(α / 7)+2, where the units of α and β are degrees.

8. The intelligent sensing and control system for vehicle lights according to claim 1, characterized in that: The steps of the steering follow-up algorithm are as follows: Step 1: Multi-source signal acquisition and synchronization: The system collects data from different sensors in real time: Vehicle dynamic signals: The vehicle's yaw rate is obtained from the attitude sensing unit; Driver intention signals: Steering wheel angle and vehicle speed are obtained via the vehicle's CAN bus; Assistive intent signal: Collects turn signal signals as direct evidence of the driver's lane-changing intention; Step 2: Signal Filtering and Preprocessing The collected raw signals are filtered to eliminate high-frequency vehicle vibration and signal noise during high-speed driving; Step 3: Driving Scene Recognition The algorithm analyzes and processes the multi-source signals to make intelligent judgments on the current driving scenario, and calls the corresponding preset differentiated control strategies based on the determined driving scenario. Step 5: Calculation and output of target deflection angle: Based on the selected strategy, the horizontal deflection angle of the target lamp head is calculated; at the same time, the calculation result is fused with the prediction information provided by the map collaboration algorithm, and the final optimal command is output to the lamp head drive control unit. Step Six: Command Execution and Closed-Loop Control The lamp head drive control unit receives the command and drives the horizontal steering motor to rotate to the target angle; Step 7: Continuous Cyclic Monitoring: The above process runs in a loop with a cycle of milliseconds.

9. The intelligent sensing and control system for vehicle lights according to claim 1, characterized in that: The steps of the map collaboration algorithm are as follows: Step 1: Obtain geographic location and map data: The system obtains the vehicle's real-time precise location, heading, and speed through the positioning module; at the same time, it requests and receives prediction information data packets of the road within a preset distance ahead of the vehicle from the high-precision digital map service through the vehicle-mounted network system. Step 2: Extracting and analyzing road feature information: The algorithm parses the received map data packets and extracts key road feature information; Step 3: Calculate spatial distance and time estimate Based on the vehicle's current position, heading, and the coordinates of feature points on the road ahead, the algorithm calculates the straight-line distance or path distance from the vehicle to the preset feature point; combined with the current real-time vehicle speed, it estimates the time it will take for the vehicle to reach that point. Step 4: Generate the predicted target angle: Based on the extracted road features, calculate the ideal prediction angle for the light head: For curves, a target horizontal deflection angle is obtained based on the radius of curvature and vehicle speed using a preset lookup table or calculation formula. For the slope, a target pitch compensation angle is derived based on the slope and the rate of change of the slope. Step 5: Generate a smooth control command sequence Based on the estimated time calculated in step three, a smooth transition trajectory from the current angle of the lamp head to the predicted target angle is generated. Step Six: Arbitration of Fusion with Real-Time Sensor Data The generated smooth prediction command is sent to the fusion unit of the decision layer and weighted and fused with the command calculated by the slope compensation algorithm and the steering follow-up algorithm based on real-time gyroscope data. Weight allocation: The system dynamically allocates weights based on the distance to the target features; Conflict arbitration: When map prediction information conflicts with real-time sensor information, arbitration is conducted according to a preset priority arbitration mechanism; Step 7: Output final instructions and fault degradation The final, merged command is sent to the lamp head drive control unit for execution; Throughout the process, the system continuously monitors the validity of the map data; Once a GPS signal loss or map data interruption is detected, the algorithm will automatically and seamlessly degrade to pure sensor mode to ensure that the most basic adaptive lighting function is always available. Step 8: Repeated execution: The above process continues to repeat as the vehicle's position changes.

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