Light carpet projection system of intelligent self-closed-loop vehicle lamp and control method therefor

Abstract

A light carpet projection system of an intelligent self-closed-loop vehicle lamp and a control method therefor, belonging to the technical field of vehicle lighting. Said light carpet projection system comprises a sensor module, a data processing module and a control execution module. The data processing module is used for receiving data of roads ahead of a host vehicle, driving data of the host vehicle and surrounding environment data of the host vehicle which are acquired by the sensor module, performing data fusion on the data of the roads ahead of the host vehicle, the driving data of the host vehicle and the surrounding environment data of the host vehicle, calculating an actual light carpet projection distance by means of an adaptive light carpet projection algorithm, and then sending the actual light carpet projection distance data to the control execution module. Said light carpet projection system and said control method have high adaptability, achieve real-time and dynamic light carpet projection of the intelligent vehicle lamp, and significantly improve the driving safety of vehicles in complex driving environments such as bumpy roads, uphill and downhill slopes and bad weather.

Description

A light carpet projection system and control method for intelligent self-closed-loop vehicle lights Technical Field

[0001] This invention relates to a light carpet projection system and control method for an intelligent self-closed-loop vehicle light, belonging to the field of automotive lighting technology. Background Technology

[0002] Currently, with the rapid development of intelligent driving technology, people's requirements for automotive lighting and safety assistance systems are increasing. Traditional vehicle lighting systems can no longer meet the needs of complex and ever-changing traffic environments, especially at night and in adverse weather conditions, where vehicle lights have limited ability to perceive and respond to the road environment ahead.

[0003] While existing technologies have proposed light carpet projection and lane line recognition, most suffer from problems such as simple algorithms, poor adaptability, and insufficient functionality. Current technologies typically design light carpet projection as a static form, failing to adjust it in real-time to reflect changes in actual lane lines and the vehicle's driving environment. These changes include straight sections turning into curves and lane narrowing, resulting in a less than ideal intelligent driving experience. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide an intelligent self-closed-loop vehicle light carpet projection system and control method, which can accurately perceive the road ahead and the surrounding environment of the vehicle, dynamically respond to complex road conditions, and adjust the vehicle light projection position and lane line fitting effect in real time according to various influencing factors. It has strong adaptability and realizes the real-time and dynamic intelligent vehicle light carpet projection, which significantly improves the driving safety of the vehicle in complex driving environments such as bumpy roads, uphill and downhill slopes and bad weather.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0006] The present invention provides a light carpet projection system for intelligent self-closed-loop vehicle lights, which includes a sensor module, a data processing module and a control execution module;

[0007] The sensor module is used to collect data on the road ahead of the vehicle, the vehicle's driving data, and the surrounding environment of the vehicle.

[0008] The data processing module is used to receive the road data in front of the vehicle, the vehicle driving data, and the vehicle surrounding environment data collected by the sensor module, perform data fusion on the road data in front of the vehicle, the vehicle driving data, and the vehicle surrounding environment data, calculate the actual distance of the light carpet projection through the adaptive light carpet projection algorithm, and then send the actual distance data of the light carpet projection to the control execution module.

[0009] The control execution module is used to control the light carpet projection distance of the vehicle lights based on the actual distance data of the light carpet projection.

[0010] Furthermore, the autonomous vehicle driving data includes autonomous vehicle speed, autonomous vehicle pitch angle, autonomous vehicle roll angle, autonomous vehicle roll angle, and autonomous vehicle yaw angle.

[0011] Another aspect of the present invention provides a control method for a light carpet projection system of an intelligent self-closed-loop vehicle light, which includes the following steps:

[0012] Step S1: Collect data on the road ahead of the vehicle, the vehicle's driving data, and the surrounding environment data through the sensor module;

[0013] Step S2: The data processing module fuses the data of the road ahead, driving data and surrounding environment collected by the sensor module, and then calculates the actual distance of the light carpet projection using the adaptive light carpet projection algorithm.

[0014] Step S3: The control execution module controls the light carpet projection distance of the vehicle lights according to the actual distance of the light carpet projection.

[0015] Furthermore, the calculation formula for the adaptive light carpet projection algorithm is as follows: P proj =P init +V×t×f(θ,α,k h (weather);

[0016] Among them, P proj This represents the actual distance of the light carpet projection.

[0017] P init The initial distance for the projection of the light carpet;

[0018] V is the speed of the vehicle;

[0019] t is the base projection time based on the distance to the vehicle in front and the speed of the vehicle itself;

[0020] f(θ,α,k h, weather) is the function to correct the distance of the light blanket projection.

[0021] Furthermore, the calculation formula for the light carpet projection distance correction function is as follows: f(θ,α,k) h ,weather)=VehicleAttitude(θ,α)×RoadSlope(k h )×WeatherCondition(weather);

[0022] Where VehicleAttitude(θ,α) is the vehicle attitude correction function;

[0023] RoadSlope(k h ) is the road surface slope correction function;

[0024] WeatherCondition(weather) is a weather condition correction function.

[0025] Furthermore, the calculation steps of the vehicle attitude correction function include:

[0026] Calculate the vehicle attitude correction function based on the vehicle's pitch and roll angles;

[0027] The calculation formula for the vehicle attitude correction function is as follows:

[0028] Where θ is the current pitch angle of the vehicle;

[0029] α is the current roll angle of the vehicle;

[0030] θ max This represents the maximum pitch angle of the vehicle.

[0031] α max This represents the maximum value of the vehicle's roll angle.

[0032] Furthermore, the calculation steps of the road surface slope correction function include:

[0033] Calculate the road slope correction function based on the road slope value;

[0034] The formula for calculating the road surface slope correction function is as follows:

[0035] Where k is an empirical coefficient;

[0036] k h It is the road surface slope value.

[0037] Furthermore, the calculation steps of the weather condition correction function include:

[0038] Calculate the correction functions for sunny days, rainy days, foggy days, and snowy days based on the weather conditions.

[0039] The calculation formulas for the correction functions for sunny days, rainy days, foggy days, and snowy days are as follows: WeatherCondition(“clear”) = 1; WeatherCondition(“rainy”) = C rainy ; WeatherCondition("foggy")=C foggy ; WeatherCondition("snowy")=Csnowy ;

[0040] The formulas for calculating the correction factors for rainy, foggy, and snowy days are as follows:

[0041] According to Beer-Lambert's law: I(z) = I0exp(-σz);

[0042] The formulas for calculating the attenuation coefficients for rainy, foggy, and snowy days are as follows:

[0043] Where WeatherCondition(“clear”) is the correction factor function for sunny days;

[0044] WeatherCondition(“rainy”) is a correction factor function for rainy days;

[0045] WeatherCondition(“foggy”) is a correction factor function for foggy weather;

[0046] WeatherCondition(“snowy”) is a correction factor function for snowy weather;

[0047] C rainy C foggy C snowy These are correction factors for rainy days, foggy days, and snowy days, respectively.

[0048] I0 is the initial illuminance of the vehicle headlight projection;

[0049] z represents the test height for the light intensity of the vehicle headlight projection;

[0050] I(z) represents the projected light intensity of the vehicle headlight at a test height of z;

[0051] I clear The reference illuminance for testing the projection of the vehicle headlights at a height of z on a sunny day;

[0052] I rainy The intensity of the projected light from the vehicle headlights at a height of z was measured during rainy weather.

[0053] I foggy The intensity of the projected light from the vehicle headlights at a height of z was measured in foggy weather.

[0054] I snowy The intensity of the projected light from the vehicle headlights at a height of z was tested during snowy weather.

[0055] σ is the atmospheric attenuation coefficient;

[0056] σ rainy σ foggyσ snowy These are the atmospheric attenuation coefficients for rainy days, foggy days, and snowy days, respectively.

[0057] By adopting the above technical solution, the present invention has the following beneficial effects:

[0058] By integrating sensor modules into the headlights, the vehicle can accurately perceive the road ahead and the surrounding environment. Through a data processing module, an adaptive light carpet projection algorithm can be used to calculate the actual distance of the light carpet projection. This algorithm takes into account the vehicle's posture, road slope, and weather conditions. It can dynamically respond to complex road conditions based on various influencing factors such as the vehicle's pitch angle, roll angle, road slope, and different weather conditions, and control the light carpet projection distance. This enables real-time adjustment of the headlight projection position and lane line fitting effect, achieving real-time and dynamic intelligent headlight light carpet projection. It has strong adaptability and significantly improves driving safety in complex driving environments such as bumpy roads, inclines, and inclement weather. Attached Figure Description

[0059] Figure 1 is a schematic block diagram of the light carpet projection system of the intelligent self-closed-loop vehicle light of the present invention.

[0060] Figure 2 is a flowchart of the control method of the light carpet projection system of the intelligent self-closed-loop vehicle light of the present invention. Detailed Implementation

[0061] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings.

[0062] Example 1

[0063] As shown in Figure 1, this embodiment provides a light carpet projection system for intelligent self-closed-loop vehicle lights, which includes a sensor module, a data processing module, and a control execution module;

[0064] The sensor module is used to collect data on the road ahead of the vehicle, the vehicle's driving data, and the surrounding environment of the vehicle;

[0065] The data processing module is used to receive the road data in front of the vehicle, the vehicle driving data, and the surrounding environment data collected by the sensor module. It performs data fusion on the road data in front of the vehicle, the vehicle driving data, and the surrounding environment data, calculates the actual distance of the light carpet projection through the adaptive light carpet projection algorithm, and then sends the actual distance data of the light carpet projection to the control execution module.

[0066] The control execution module is used to control the light carpet projection distance of the vehicle lights based on the actual distance data of the light carpet projection.

[0067] The autonomous vehicle driving data in this embodiment includes autonomous vehicle speed, autonomous vehicle pitch angle, autonomous vehicle roll angle, autonomous vehicle roll angle, and autonomous vehicle yaw angle.

[0068] Example 2

[0069] As shown in Figure 2, this embodiment provides a control method for a light carpet projection system of an intelligent self-closed-loop vehicle light as described in Embodiment 1, which includes the following steps:

[0070] Step S1: Collect data on the road ahead of the vehicle, the vehicle's driving data, and the surrounding environment data through the sensor module;

[0071] Step S2: The data processing module fuses the data of the road ahead, driving data and surrounding environment collected by the sensor module, and then calculates the actual distance of the light carpet projection using the adaptive light carpet projection algorithm.

[0072] Step S3: The control execution module controls the light carpet projection distance of the vehicle lights according to the actual distance of the light carpet projection.

[0073] The calculation formula for the adaptive light carpet projection algorithm in this embodiment is as follows: P proj =P init +V×t×f(θ,α,k h (weather);

[0074] Among them, P proj This represents the actual distance of the light carpet projection.

[0075] P init The initial distance for the projection of the light carpet;

[0076] V is the speed of the vehicle;

[0077] t is the basic projection time based on the distance to the vehicle in front and the speed of the vehicle itself. It represents the basic time required for the light carpet projection image to reach the hypothetical or preset target position under the current vehicle speed and distance to the vehicle in front.

[0078] f(θ,α,k h, The weather function is a correction function for the projection distance of the light carpet based on factors such as vehicle attitude, road slope, and weather conditions. It contains several complex nonlinear calculation steps.

[0079] The calculation formula for the light carpet projection distance correction function in this embodiment is as follows: f(θ,α,k) h ,weather)=VehicleAttitude(θ,α)×RoadSlope(k h )×WeatherCondition(weather);

[0080] Where VehicleAttitude(θ,α) is the vehicle attitude correction function;

[0081] RoadSlope(k h ) is the road surface slope correction function;

[0082] WeatherCondition(weather) is a weather condition correction function.

[0083] The calculation steps of the vehicle attitude correction function in this embodiment include:

[0084] The pitch and roll angles of the vehicle directly affect the projection distance of the light carpet. The larger the pitch angle of the vehicle, the smaller the projection distance of the light carpet should be. The larger the roll angle of the vehicle, the smaller the projection distance of the light carpet should also be. Therefore, the attitude correction function of the vehicle is calculated based on the pitch and roll angles of the vehicle.

[0085] The formula for calculating the vehicle attitude correction function is as follows:

[0086] Where θ is the current pitch angle of the vehicle, which varies with the degree of forward and backward tilt of the vehicle;

[0087] α is the current roll angle of the vehicle, which varies with the degree of left and right tilt of the vehicle;

[0088] θ max This represents the maximum pitch angle of the vehicle.

[0089] α max This represents the maximum value of the vehicle's roll angle;

[0090] Specifically, θ max and α max The determination of the value needs to take into account a variety of factors such as the vehicle's structural strength, suspension system performance, tire grip, and driving environment. Through precise measurement and calculation, a suitable maximum value is set for each model to ensure the accuracy and effectiveness of the vehicle attitude correction function.

[0091] The calculation steps for the road slope correction function in this embodiment include:

[0092] The greater the road surface slope value, the more likely the actual projection effect will be affected, requiring correction of relevant parameters. Therefore, a road surface slope correction function is calculated based on the road surface slope value.

[0093] The formula for calculating the road surface slope correction function is as follows:

[0094] Where k is an empirical coefficient used to adjust the degree of influence of road surface slope on the projection effect;

[0095] k hIt is the road surface slope value.

[0096] The calculation steps of the weather condition correction function in this embodiment include:

[0097] Considering the impact of weather conditions on the projection effect, correction functions for sunny days, rainy days, foggy days, and snowy days are calculated separately according to different weather conditions.

[0098] The formulas for calculating the correction functions for sunny days, rainy days, foggy days, and snowy days are as follows: WeatherCondition(“clear”) = 1; WeatherCondition(“rainy”) = C rainy ; WeatherCondition("foggy")=C foggy ; WeatherCondition("snowy")=C snowy ;

[0099] Specifically, determining the selection of weather parameters is a relatively complex process because it involves a variety of factors, including light scattering, absorption, reflection, and suspended particles in the atmosphere. In order to determine the correction function more accurately, this embodiment adopts a method based on a combination of actual measurements and theoretical models.

[0100] Assuming that the projection effect (such as brightness and contrast) is 1 (i.e., the baseline value) in sunny weather, the projection effect in rainy, foggy, and snowy weather will be affected by the attenuation coefficient. The correction coefficient for rainy, foggy, and snowy weather can be defined as the ratio of the projection effect in sunny weather to the corresponding projection effect in rainy, foggy, and snowy weather. Therefore, the formulas for calculating the correction coefficient for rainy, foggy, and snowy weather are as follows:

[0101] In clear weather, light travels almost in a straight line. However, in rainy, foggy, or snowy weather, light is attenuated by scattering and absorption by water droplets, fog droplets, or snowflakes. To address this attenuation, the Beer-Lambert law is introduced: I(z)=I0exp(-σz).

[0102] For different weather conditions, it is necessary to determine the corresponding attenuation coefficient. This can be achieved by measuring the change in the intensity of the projected light from the vehicle headlights at a fixed distance. Therefore, the calculation formulas for the attenuation coefficients for rainy days, foggy days, and snowy days are as follows:

[0103] Where WeatherCondition(“clear”) is the correction factor function for sunny days;

[0104] WeatherCondition(“rainy”) is a correction factor function for rainy days, and

[0105] 0<WeatherCondition("rainy")<1;

[0106] WeatherCondition(“foggy”) is a correction factor function for foggy weather, and

[0107] 0<WeatherCondition("foggy")<1;

[0108] WeatherCondition(“snowy”) is a correction factor function for snowy weather, and

[0109] 0<WeatherCondition("snowy")<1;

[0110] C rainy C foggy C snowy These are correction factors for rainy days, foggy days, and snowy days, respectively.

[0111] I0 is the initial illuminance of the vehicle headlight projection;

[0112] z represents the test height for the light intensity of the vehicle headlight projection;

[0113] I(z) represents the projected light intensity of the vehicle headlight at a test height of z;

[0114] I clear The reference illuminance for testing the projection of the vehicle headlights at a height of z on a sunny day;

[0115] I rainy The intensity of the projected light from the vehicle headlights at a height of z was measured during rainy weather.

[0116] I foggy The intensity of the projected light from the vehicle headlights at a height of z was measured in foggy weather.

[0117] I snowy The intensity of the projected light from the vehicle headlights at a height of z was tested during snowy weather.

[0118] σ is the atmospheric attenuation coefficient, which is related to weather conditions;

[0119] σ rainy σ foggy σ snowy These are the atmospheric attenuation coefficients for rainy days, foggy days, and snowy days, respectively.

[0120] In summary, assuming the following conditions: the current vehicle's pitch angle θ = 10°, the current vehicle's roll angle α = 5°, and the road slope k...h =5°, weather conditions are rainy, and the parameter θ is known. max =45°, α max =10°, k=0.1, C rainy =0.8. Substituting the above data into the light carpet projection distance correction function, the calculation is as follows: f(θ,α,k) h, weather)=f(10,5,5,'rainy')=(1-10 / 45)×(1-5 / 10)×(1 / (1+0.1×5)×0.8)≈0.15;

[0121] Substituting the calculated light carpet projection distance correction value of 0.15 into the adaptive light carpet projection algorithm, the actual light carpet projection distance can be calculated.

[0122] Assume the initial distance P of the light blanket projection init Given that the distance in front of the vehicle is 7 meters, the vehicle's speed V is 60 km / h (equivalent to 16.67 m / s after unit conversion), t is 0.2 seconds, and f is 0.15, then the actual distance P of the light carpet projection is... proj =7 + 16.67 × 0.2 × 0.15 ≈ 7.5 meters.

[0123] The working principle of this invention is as follows:

[0124] The sensor module collects data on the road ahead, driving data, and the surrounding environment. The data processing module then fuses these data, and based on the vehicle's posture, road slope, and weather conditions, calculates the actual distance of the light carpet projection using an adaptive light carpet projection algorithm. Finally, the control execution module controls the headlights to adjust the light carpet projection distance according to the actual distance.

[0125] By integrating sensor modules into the headlights, the vehicle can accurately perceive the road ahead and the surrounding environment. Through a data processing module, an adaptive light carpet projection algorithm can be used to calculate the actual distance of the light carpet projection. This algorithm takes into account the vehicle's posture, road slope, and weather conditions. It can dynamically respond to complex road conditions based on various influencing factors such as the vehicle's pitch angle, roll angle, road slope, and different weather conditions, and control the light carpet projection distance. This enables real-time adjustment of the headlight projection position and lane line fitting effect, achieving real-time and dynamic intelligent headlight light carpet projection. It has strong adaptability and significantly improves driving safety in complex driving environments such as bumpy roads, inclines, and inclement weather.

[0126] The specific embodiments described above further illustrate the technical problems, technical solutions, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A light carpet projection system for intelligent self-closing loop vehicle lights, characterized in that, It includes a sensor module, a data processing module, and a control execution module; The sensor module is used to collect data on the road ahead of the vehicle, the vehicle's driving data, and the surrounding environment of the vehicle. The data processing module is used to receive the road data in front of the vehicle, the vehicle driving data, and the vehicle surrounding environment data collected by the sensor module, perform data fusion on the road data in front of the vehicle, the vehicle driving data, and the vehicle surrounding environment data, calculate the actual distance of the light carpet projection through the adaptive light carpet projection algorithm, and then send the actual distance data of the light carpet projection to the control execution module. The control execution module is used to control the light carpet projection distance of the vehicle lights based on the actual distance data of the light carpet projection. The autonomous vehicle driving data includes autonomous vehicle speed, autonomous vehicle pitch angle, autonomous vehicle roll angle, autonomous vehicle roll angle, and autonomous vehicle yaw angle; The control method for the light carpet projection system of the intelligent self-closed-loop vehicle lights includes the following steps: Step S1: Collect data on the road ahead of the vehicle, the vehicle's driving data, and the surrounding environment data through the sensor module; Step S2: The data processing module fuses the data of the road ahead, driving data and surrounding environment collected by the sensor module, and then calculates the actual distance of the light carpet projection using the adaptive light carpet projection algorithm. Step S3: The control execution module controls the light carpet projection distance of the vehicle lights according to the actual distance of the light carpet projection. The calculation formula for the adaptive light carpet projection algorithm is as follows: P.S proj JP init +V×t×f(θ,α,k h ,weather) Among them, P proj This represents the actual distance of the light carpet projection. P init The initial distance for the projection of the light carpet; V is the speed of the vehicle; t is the base projection time based on the distance to the vehicle in front and the speed of the vehicle itself; f(θ,α,k h, weather) is the correction function for the projection distance of the light blanket; θ is the current pitch angle of the vehicle; α is the current roll angle of the vehicle; k h It is the road surface slope value; Weather refers to weather conditions.

2. The light carpet projection system for intelligent self-closed-loop vehicle lights according to claim 1, characterized in that, The formula for calculating the projection distance correction function of the light carpet is as follows: f(θ,α,k) h ,weather)=VehicleAttitude(θ,α)×RoadSlope(k h )×WeatherCondition(weather); Where VehicleAttitude(θ,α) is the vehicle attitude correction function; RoadSlope(k h ) is the road surface slope correction function; WeatherCondition(weather) is a weather condition correction function.

3. The light carpet projection system for intelligent self-closed-loop vehicle lights according to claim 2, characterized in that, The calculation steps for the vehicle attitude correction function include: Calculate the vehicle attitude correction function based on the vehicle's pitch and roll angles; The calculation formula for the vehicle attitude correction function is as follows: Where θ is the current pitch angle of the vehicle; α is the current roll angle of the vehicle; θ max This represents the maximum pitch angle of the vehicle. α max This represents the maximum value of the vehicle's roll angle.

4. The light carpet projection system for intelligent self-closed-loop vehicle lights according to claim 3, characterized in that, The calculation steps for the road surface slope correction function include: Calculate the road slope correction function based on the road slope value; The formula for calculating the road surface slope correction function is as follows: Where k is an empirical coefficient; k h It is the road surface slope value.

5. The light carpet projection system for intelligent self-closed-loop vehicle lights according to claim 4, characterized in that, The calculation steps for the weather condition correction function include: Calculate the correction functions for sunny days, rainy days, foggy days, and snowy days based on the weather conditions. The calculation formulas for the correction functions for sunny days, rainy days, foggy days, and snowy days are as follows: WeatherCondition("clear")=1; WeatherCondition("rainy”)=C rainy ; WeatherCondition("foggy”)=C foggy ; WeatherCondition("snowy”)=C snowy ; The formulas for calculating the correction factors for rainy, foggy, and snowy days are as follows: According to Beer-Lambert's law: I(z) = I0exp(-σz); The formulas for calculating the attenuation coefficients for rainy, foggy, and snowy days are as follows: Among them, WeatherCondition("clear") is the correction factor function for sunny days; WeatherCondition("rainy") is a correction factor function for rainy days; WeatherCondition("foggy") is a correction factor function for foggy weather; WeatherCondition("snowy") is a correction factor function for snowy weather; C rainy C foggy C snowy These are correction factors for rainy days, foggy days, and snowy days, respectively. I0 is the initial illuminance of the vehicle headlight projection; z represents the test height for the light intensity of the vehicle headlight projection; I(z) represents the projected light intensity of the vehicle headlight at a test height of z; I clear The reference illuminance for testing the projection of the vehicle headlights at a height of z on a sunny day; I rainy The intensity of the projected light from the vehicle headlights at a height of z was measured during rainy weather. I foggy The intensity of the projected light from the vehicle headlights at a height of z was measured in foggy weather. I snowy The intensity of the projected light from the vehicle headlights at a height of z was tested during snowy weather. σ is the atmospheric attenuation coefficient; σ rainy σ foggy σ snowy These are the atmospheric attenuation coefficients for rainy days, foggy days, and snowy days, respectively.

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