Intelligent environment perception and dynamic compensation system for car lamp

Abstract

The utility model discloses an intelligent environment perception and dynamic compensation system for a vehicle lamp. The intelligent environment perception and dynamic compensation system for the vehicle lamp comprises a DC-DC power supply, a sensor module assembly, a control unit and a dynamic adjustment executing mechanism, the DC-DC power supply supplies power to the whole system; the control unit is a single-chip microcomputer. The sensor module integration comprises an infrared camera, a millimeter wave radar, a rainfall sensor and a humidity sensor which are respectively in communication connection with the single chip microcomputer; the dynamic adjustment executing mechanism comprises a stepping motor and an LED driving chip which are in communication connection with the single-chip microcomputer and controlled by the single-chip microcomputer. The stepping motor is connected with a lens of the automobile lamp and used for controlling deflection of the lens. And the LED driving chip is used for controlling 256-level independent dimming and color temperature adjustment of the single lamp bead. Closed-loop control of environment perception and light beam compensation is realized through multi-sensor fusion and edge calculation, and the safety and the response speed in a complex scene are improved.

Description

Technical Field

[0001] This utility model relates to the field of automotive lighting technology, specifically to an intelligent environmental perception and dynamic compensation system for vehicle lights. Background Technology

[0002] With the increasing prevalence of automobiles and intelligent driving, the ways in which vehicle lights interact are becoming more numerous and complex. As intelligent driving technology continues to evolve, vehicle lights also need to achieve intelligence to enable vehicle-machine collaboration. Without human intervention, vehicle lights should be able to intelligently perceive the environment and autonomously switch between high and low beams, turn signals, fog lights, and reversing lights, etc. Early vehicle lights (kerosene lamps, acetylene lamps) only met basic lighting needs, with low brightness and poor reliability. While incandescent and halogen lamps achieved electrified lighting, they had low luminous efficiency, high energy consumption, and could not adapt to complex road conditions. Existing vehicle light solutions use matrix LED zone control to avoid glare, but this relies on high-performance chips (such as TI DLP), increasing costs by more than 40%, and lacks color temperature adjustment in rain and fog scenarios.

[0003] Existing technical problems: 1. Insufficient environmental adaptability: Traditional LED vehicle lights use a fixed light pattern, which causes severe scattering in rainy and foggy weather, reducing penetration by 30%-50%; 2. Dynamic response delay: The adaptive steering system relies on the steering wheel angle signal, and the curve prediction error reaches 2°-3°, with a response time of over 100ms; 3. Low sensor reliability: A single camera or photosensitive sensor is easily affected by strong light and dirt, resulting in a false judgment rate of over 20%. Summary of the Invention

[0004] The problem this invention aims to solve is to address the shortcomings of the existing technology by proposing an improved or replacement solution, particularly an intelligent vehicle lighting environment perception system integrating an environmental perception sensor and a dynamic compensation algorithm. This system is especially suitable for adaptive beam adjustment in adverse weather conditions (rain, fog, snow) and complex road conditions (curves, oncoming vehicles). It addresses the issues of severe beam scattering and insufficient penetration in existing vehicle lighting systems during rain and fog; optimizes the adaptive beam scheme for curves; and avoids the problem of single sensors being susceptible to environmental interference (strong light, dirt), leading to misjudgments.

[0005] To solve the above problems, the present invention adopts the following solution: a vehicle headlight intelligent environmental perception and dynamic compensation system, characterized in that the vehicle headlight intelligent environmental perception and dynamic compensation system includes a DC-DC power supply, a sensor module integration, a control unit, and a dynamic adjustment actuator; the DC-DC power supply powers the entire system; the control unit is a microcontroller; the sensor module integration includes an infrared camera, a millimeter-wave radar, a rain sensor, and a humidity sensor, and is respectively communicatively connected to the microcontroller; the infrared camera is used to identify the outline of obstacles in front, the millimeter-wave radar is used to detect the distance to obstacles, the rain sensor is used to detect the intensity of precipitation, and the humidity sensor is used to determine the fog concentration; the dynamic adjustment actuator includes a stepper motor and an LED driver chip, and is respectively communicatively connected to the microcontroller and controlled by the microcontroller; the stepper motor is connected to the lens of the vehicle headlight and is used to control the lens deflection; the LED driver chip is used to control 256 levels of independent dimming and color temperature adjustment for each LED bead.

[0006] Furthermore, the intelligent environmental perception and dynamic compensation system for vehicle lights is characterized by further including a GPS / IMU positioning module, which is connected to a microcontroller to provide vehicle position and heading angle.

[0007] Furthermore, the intelligent environmental perception and dynamic compensation system for vehicle lights is characterized in that the microcontroller supports CAN communication, SPI communication, IIC communication, and LIN communication; the microcontroller interacts with the vehicle controller via CAN communication; the microcontroller is integrated with the sensor module via SPI communication and IIC communication, and communicates with the dynamic adjustment actuator via LIN communication.

[0008] Furthermore, the vehicle headlight intelligent environmental perception and dynamic compensation system is characterized in that the infrared camera supports HDR and infrared night vision, and is composed of a camera and an infrared sensor; the infrared camera and the rain sensor are installed inside the headlight housing and are coated with a hydrophobic coating for protection.

[0009] Furthermore, the intelligent environmental perception and dynamic compensation system for vehicle lights is characterized in that the microcontroller is equipped with an NPU to enhance the microcontroller's AI collaborative image processing capabilities. The system also includes a lateral blind spot radar, which, together with the main radar, forms a dual verification mechanism to prevent misjudgments caused by the failure of a single sensor.

[0010] The technical effects of this utility model are as follows: By using multi-sensor fusion and edge computing, closed-loop control of environmental perception and beam compensation is achieved, thereby improving safety and response speed in complex scenarios.

[0011] In rain and fog scenarios, the effective illumination distance of the beam is increased by 35% (from 60m to 81m); the prediction error of beam deflection on curves is ≤0.5°, and the response delay is ≤30ms; the glare suppression rate for oncoming vehicles is >90%.

[0012] Multi-sensor redundancy design improves the reliability of environmental perception; edge computing (local NPU) avoids cloud dependence and meets real-time requirements. Attached Figure Description

[0013] Figure 1 This is a structural diagram of the vehicle lighting intelligent environmental perception and dynamic compensation system. Detailed Implementation

[0014] The present invention will now be described in further detail with reference to the accompanying drawings. Example

[0015] The vehicle headlight intelligent environmental perception and dynamic compensation system includes a DC-DC power supply, an integrated sensor module, a control unit, and a dynamic adjustment actuator. The DC-DC power supply powers the entire system. The control unit is a microcontroller. The integrated sensor module includes an infrared camera, a millimeter-wave radar, a rain sensor, and a humidity sensor, all of which are communicatively connected to the microcontroller. The infrared camera is used to identify the outline of obstacles ahead, the millimeter-wave radar is used to detect the distance to obstacles, the rain sensor is used to detect precipitation intensity, and the humidity sensor is used to determine fog concentration. The dynamic adjustment actuator includes a stepper motor and an LED driver chip, both of which are communicatively connected to and controlled by the microcontroller. The stepper motor is connected to the headlight lens to control lens deflection. The LED driver chip controls 256 levels of independent dimming and color temperature adjustment for each LED.

[0016] I. Hardware Implementation

[0017] 1. Sensor module integration

[0018] Multimodal sensor array:

[0019] The forward-facing camera uses the Omnivision OV10640, which supports HDR+ infrared night vision. It is co-packaged with the rain sensor in the headlight housing and uses a hydrophobic coating to reduce dirt interference.

[0020] Millimeter-wave radar (detection range 50-150m) is integrated with GPS / IMU modules for cross-validation of curve curvature and vehicle attitude.

[0021] The vehicle lighting controller integrates multiple sensor modules, including an infrared camera circuit, a millimeter-wave radar circuit, a rain / humidity sensor circuit, and a GPS / IMU positioning module.

[0022] Redundancy design: The side-mounted blind spot radar and the main radar form a dual verification to avoid misjudgment caused by the failure of a single sensor.

[0023] 2. Control Unit Design

[0024] The main control chip is the NXP S32K144, which supports CAN FD communication (5Mbps) to achieve synchronous processing of data from multiple sensors.

[0025] The AI ​​coprocessor, equipped with NXP's NPU, processes image data and performs beam deflection prediction.

[0026] 3. Dynamic adjustment of the actuator

[0027] A stepper motor drives the lens deflection (accuracy ±0.1°), supporting cornering compensation angles of 8°-15°.

[0028] The LED driver circuit is based on an LED driver chip, enabling 256 levels of independent dimming for a single LED bead, and simultaneously supporting color temperature adjustment (3000K-6500K).

[0029] II. Software Logic

[0030] Multi-sensor data fusion algorithm (Kalman filter + neural network denoising).

[0031] Dynamic compensation decision engine (generates PWM duty cycle, color temperature, and beam deflection instructions based on environmental data).

[0032] III. Dynamic Compensation Algorithm Based on Intelligent Environmental Perception and Dynamic Compensation System for Vehicle Lighting

[0033] 1. Dynamic compensation in rain and fog scenes:

[0034] Step 1: Rain gauges detect precipitation intensity, and humidity sensors determine fog concentration;

[0035] Step 2: The infrared camera identifies the outline of the obstacle in front, and the millimeter-wave radar verifies the target distance;

[0036] Step 3: The control module calculates the scattering compensation coefficient, constructs the environmental state matrix, and integrates the parameters of rainfall intensity (Rrain), fog concentration (Hfog), and target distance (Dtarget) to generate a dynamic compensation coefficient.

[0037]

[0038] Step 4: Adjust the LED color temperature (from 6500K to 3000K) and expand the beam scattering angle (by driving the lens outward by 5°-15° using a stepper motor).

[0039] 2. Curve prediction and follow-up compensation:

[0040] Step 1: GPS / IMU acquires vehicle position and heading angle, and navigation map pre-extracts the curvature of the curve ahead;

[0041] Step 2: By inputting vehicle speed, steering wheel angle, and navigation map curvature, output the beam advance deflection angle:

[0042]

[0043] Step 3: The stepper motor drives the lens to deflect, simultaneously enhancing the brightness of the LEDs on the inside of the curve (duty cycle increased by 20%-40%).

[0044] 3. Anti-glare for oncoming vehicles:

[0045] Step 1: The camera identifies the position of the oncoming headlights, and the radar confirms the relative speed;

[0046] Step 2: Activate the LEDs in the corresponding area of ​​the dynamic shielding matrix (respond within 10ms).

[0047] Step 3: Overlay compensation brightness in the unshielded area to maintain overall road illumination uniformity.

[0048] IV. Testing and Verification

[0049] 1. Measured data:

[0050] In rain and fog scenarios, the effective illumination distance of the beam is increased by 35% (from 60m to 81m).

[0051] The prediction error of the curved beam deflection is ≤0.5°, and the response delay is ≤30ms;

[0052] Glare suppression rate against oncoming vehicles >90%.

[0053] 2. Summary of Advantages:

[0054] Multi-sensor redundancy design improves the reliability of environmental perception;

[0055] Edge computing (local NPU) avoids cloud dependency and meets real-time requirements.

Claims

1. A vehicle headlight intelligent environmental perception and dynamic compensation system, characterized in that, The vehicle headlight intelligent environmental perception and dynamic compensation system includes a DC-DC power supply, sensor module integration, control unit, and dynamic adjustment actuator. The DC-DC power supply powers the entire system. The control unit is a microcontroller. The sensor module integration includes an infrared camera, millimeter-wave radar, rain sensor, and humidity sensor, all of which are communicatively connected to the microcontroller. The infrared camera is used to identify the outline of obstacles ahead, the millimeter-wave radar is used to detect the distance to obstacles, the rain sensor is used to detect precipitation intensity, and the humidity sensor is used to determine fog concentration. The dynamic adjustment actuator includes a stepper motor and an LED driver chip, both of which are communicatively connected to and controlled by the microcontroller. The stepper motor is connected to the headlight lens to control lens deflection. The LED driver chip controls 256 levels of independent dimming and color temperature adjustment for each LED.

2. The intelligent environmental perception and dynamic compensation system for vehicle lights according to claim 1, characterized in that, It also includes a GPS / IMU positioning module, which communicates with the microcontroller to provide vehicle position and heading angle.

3. The intelligent environmental perception and dynamic compensation system for vehicle lights according to claim 1, characterized in that, The microcontroller supports CAN communication, SPI communication, IIC communication, and LIN communication; the microcontroller interacts with the vehicle controller via CAN communication; the microcontroller is integrated with the sensor module via SPI communication and IIC communication, and communicates with the dynamic adjustment actuator via LIN communication.

4. The intelligent environmental perception and dynamic compensation system for vehicle lights according to claim 1, characterized in that, The infrared camera supports HDR and infrared night vision, and consists of a camera and an infrared sensor; the infrared camera and rain sensor are housed inside the lamp housing and protected by a hydrophobic coating.

5. The intelligent environmental perception and dynamic compensation system for vehicle lights according to claim 1, characterized in that, It also includes a side-to-side blind spot radar, which forms a dual verification with the main radar to avoid misjudgment caused by the failure of a single sensor.

6. The intelligent environmental perception and dynamic compensation system for vehicle lights according to claim 4, characterized in that, The microcontroller has an NPU built in it to enhance its AI collaborative image processing capabilities.

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