CN120186849B - Brake tail light control method, electronic device and medium - Google Patents
Brake tail light control method, electronic device and mediumInfo
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- CN120186849B CN120186849B CN202510653630.7A CN202510653630A CN120186849B CN 120186849 B CN120186849 B CN 120186849B CN 202510653630 A CN202510653630 A CN 202510653630A CN 120186849 B CN120186849 B CN 120186849B
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- H—ELECTRICITY
- H05—ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
- H05B—ELECTRIC HEATING; ELECTRIC LIGHT SOURCES NOT OTHERWISE PROVIDED FOR; CIRCUIT ARRANGEMENTS FOR ELECTRIC LIGHT SOURCES, IN GENERAL
- H05B47/00—Circuit arrangements for operating light sources in general, i.e. where the type of light source is not relevant
- H05B47/10—Controlling the light source
- H05B47/105—Controlling the light source in response to determined parameters
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60Q—ARRANGEMENT OF SIGNALLING OR LIGHTING DEVICES, THE MOUNTING OR SUPPORTING THEREOF OR CIRCUITS THEREFOR, FOR VEHICLES IN GENERAL
- B60Q1/00—Arrangement of optical signalling or lighting devices, the mounting or supporting thereof or circuits therefor
- B60Q1/26—Arrangement of optical signalling or lighting devices, the mounting or supporting thereof or circuits therefor the devices being primarily intended to indicate the vehicle, or parts thereof, or to give signals, to other traffic
- B60Q1/44—Arrangement of optical signalling or lighting devices, the mounting or supporting thereof or circuits therefor the devices being primarily intended to indicate the vehicle, or parts thereof, or to give signals, to other traffic for indicating braking action or preparation for braking, e.g. by detection of the foot approaching the brake pedal
- B60Q1/444—Arrangement of optical signalling or lighting devices, the mounting or supporting thereof or circuits therefor the devices being primarily intended to indicate the vehicle, or parts thereof, or to give signals, to other traffic for indicating braking action or preparation for braking, e.g. by detection of the foot approaching the brake pedal with indication of the braking strength or speed changes, e.g. by changing shape or intensity of the indication
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- H—ELECTRICITY
- H05—ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
- H05B—ELECTRIC HEATING; ELECTRIC LIGHT SOURCES NOT OTHERWISE PROVIDED FOR; CIRCUIT ARRANGEMENTS FOR ELECTRIC LIGHT SOURCES, IN GENERAL
- H05B47/00—Circuit arrangements for operating light sources in general, i.e. where the type of light source is not relevant
- H05B47/10—Controlling the light source
- H05B47/165—Controlling the light source following a pre-assigned programmed sequence; Logic control [LC]
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- Engineering & Computer Science (AREA)
- Mechanical Engineering (AREA)
- Regulating Braking Force (AREA)
Abstract
The application provides a control method of a brake taillight, electronic equipment and a medium, wherein the method comprises the steps of determining a deceleration ratio, a deceleration increment ratio and a pavement low adhesion ratio corresponding to each preset calculation path according to vehicle sensor information and preset braking factors; the method comprises the steps of determining a single-path residual braking space corresponding to a preset calculation path according to a deceleration duty ratio, a deceleration increment duty ratio and a road surface low adhesion ratio, determining a fusion coefficient according to path reliability corresponding to each preset calculation path and current working condition complexity, determining a fusion residual braking space according to the fusion coefficient and the single-path residual braking space corresponding to each preset calculation path, and controlling display brightness and display area of a tail lamp of a vehicle according to the fusion residual braking space. By the technical scheme, the dynamic control of the tail lamp is realized, the system is suitable for complex and changeable road condition environments, and visual and reliable visual warning is provided for a rear vehicle.
Description
Technical Field
The application relates to the technical field of vehicle control, in particular to a brake tail lamp control method, electronic equipment and a medium.
Background
At present, a mode of fixed brightness or simple flickering is adopted for display control of a vehicle brake tail lamp, and the mode cannot fully prompt the actual deceleration of a front vehicle and the current road surface condition to a rear vehicle.
In addition, the control of the vehicle brake taillight can be triggered and controlled according to the opening signal of the brake pedal, so that factors such as a wet road surface or sudden emergency brake change are difficult to reflect, the judgment of the deceleration of the rear vehicle to the front vehicle is delayed, and the rear-end collision is easy to occur.
Disclosure of Invention
In view of the above-mentioned drawbacks or shortcomings in the prior art, the present application aims to provide a control method, an electronic device and a medium for a brake taillight, so as to dynamically control the taillight, adapt to complex and changeable road condition environments, and provide visual and reliable visual warning for a rear vehicle.
The embodiment of the application provides a control method for a brake taillight, which comprises the following steps:
For each preset calculation path, determining a deceleration ratio, a deceleration increment ratio and a pavement low adhesion ratio corresponding to the preset calculation path according to vehicle sensor information and a preset braking factor;
determining a single-path residual braking space corresponding to the preset calculation path according to the deceleration duty ratio, the deceleration increment duty ratio and the pavement low adhesion ratio;
determining a fusion coefficient according to the path reliability corresponding to each preset calculation path and the complexity of the current working condition, and determining a fusion residual braking space according to the fusion coefficient and a single path residual braking space corresponding to each preset calculation path;
According to the fused residual braking space, controlling the display brightness and the display area of the tail lamp of the vehicle;
the preset calculation path comprises a mathematical model path and a neural network model path.
According to the technical scheme provided by the embodiment of the application, optionally, the determining the deceleration ratio, the deceleration increment ratio and the road surface low adhesion ratio corresponding to the preset calculation path according to the vehicle sensor information and the preset braking factor comprises:
Determining a road surface adhesion coefficient based on the preset calculation path according to the vehicle sensor information, and determining a road surface low adhesion ratio corresponding to the preset calculation path according to the road surface adhesion coefficient;
Determining the maximum available deceleration corresponding to the preset calculation path according to the road surface adhesion coefficient, a preset braking factor and the gravity acceleration;
determining a deceleration change rate according to the actual deceleration at the current moment and the actual deceleration at the previous moment;
Determining a deceleration ratio corresponding to the preset calculation path according to the actual deceleration at the current moment and the maximum available deceleration corresponding to the preset calculation path;
And determining the deceleration increment duty ratio corresponding to the preset calculation path according to the deceleration change rate and the maximum available deceleration corresponding to the preset calculation path.
According to the technical scheme provided by the embodiment of the application, optionally, before determining the fusion coefficient according to the path reliability corresponding to each preset calculation path and the complexity of the current working condition, the method further comprises:
Determining the change rate of the road surface attachment coefficient according to the road surface attachment coefficient at the current moment and the road surface attachment coefficient at the previous moment corresponding to the mathematical model path;
determining the environment complexity according to each environment factor;
And determining the complexity of the current working condition according to the deceleration ratio corresponding to the mathematical model path, the change rate of the road surface attachment coefficient and the environment complexity.
According to the technical solution provided in the embodiment of the present application, optionally, the determining, according to the deceleration duty ratio, the deceleration increment duty ratio, and the road surface low adhesion ratio, the single-path remaining braking space corresponding to the preset calculation path includes:
Respectively weighting the deceleration duty ratio, the deceleration increment duty ratio and the pavement low adhesion ratio according to a first coefficient, a second coefficient and a third coefficient corresponding to the preset calculation path to determine a weighted residual braking space;
If the weighted remaining braking space is greater than 1, determining that the single-path remaining braking space corresponding to the preset calculation path is 1;
If the weighted remaining braking space is greater than or equal to 0 and less than or equal to 1, taking the weighted remaining braking space as a single-path remaining braking space corresponding to the preset calculation path;
And if the weighted remaining braking space is smaller than 0, determining that the single-path remaining braking space corresponding to the preset calculation path is 0.
According to the technical scheme provided by the embodiment of the application, optionally, determining the fusion coefficient according to the path reliability corresponding to each preset calculation path and the complexity of the current working condition includes:
Determining a reliability difference value according to the path reliability corresponding to the mathematical model path and the path reliability of the neural network model path, and determining a first addend according to the reliability difference value and a preset reliability difference sensitivity;
Determining a second addend according to the complexity of the current working condition, the preset complexity and the sensitivity of the preset complexity;
And processing the sum value of the first addend and the second addend through a preset regression function to obtain a fusion coefficient.
According to the technical proposal provided by the embodiment of the application,
Before determining the fusion coefficient according to the path reliability corresponding to each preset calculation path and the complexity of the current working condition, the method further comprises the following steps:
acquiring an electronic control unit health degree and a sensor data quality evaluation value of a vehicle;
Determining the path reliability corresponding to the mathematical model path according to the health degree of the electronic control unit and the sensor data quality evaluation value;
Obtaining a result prediction difference value of a target model and a preset evaluation model in the neural network model path and the data quality of the vehicle sensor information;
Determining a model confidence corresponding to the neural network model path according to the result prediction difference value and the data quality;
and determining the path reliability corresponding to the neural network model path according to the electronic control unit health degree, the sensor data quality evaluation value and the model confidence degree.
According to the technical scheme provided by the embodiment of the application, optionally, the method for controlling the display brightness and the display area of the tail lamp of the vehicle according to the fused residual braking space comprises the following steps:
If the fused residual braking space is larger than or equal to a first threshold value, determining that the display brightness of the vehicle tail lamp is the preset minimum brightness, and determining that the display area of the vehicle tail lamp is the preset minimum area;
If the fused residual braking space is smaller than the first threshold value and larger than or equal to the second threshold value, determining a target proportion according to the fused residual braking space, the first threshold value and the second threshold value, determining the display brightness of the vehicle tail lamp according to the target proportion, the preset minimum brightness and the preset maximum brightness, and determining the display area of the vehicle tail lamp according to the target proportion, the preset minimum area and the preset maximum area;
and if the fused residual braking space is smaller than the second threshold value, determining that the display brightness of the vehicle tail lamp is the preset highest brightness, and determining that the display area of the vehicle tail lamp is the preset largest area.
According to the technical scheme provided by the embodiment of the application, optionally, the method for controlling the display brightness and the display area of the tail lamp of the vehicle according to the fused residual braking space comprises the following steps:
determining a target residual braking space at the current moment according to a preset filter coefficient, the fused residual braking space and the target residual braking space at the previous moment;
Updating the target residual braking space at the current moment based on a hysteresis mechanism;
and controlling the display brightness and the display area of the tail lamp of the vehicle according to the updated target residual braking space at the current moment.
The embodiment of the application also provides electronic equipment, which comprises:
A processor and a memory;
The processor is configured to execute the steps of the brake taillight control method according to any of the embodiments by calling a program or instructions stored in the memory.
Embodiments of the present application also provide a computer-readable storage medium storing a program or instructions that cause a computer to perform the steps of the brake taillight control method according to any of the embodiments.
In summary, the present application provides a control method for a brake taillight, by determining, for each preset calculation path, a deceleration ratio, a deceleration increment ratio, and a road surface low adhesion ratio corresponding to the preset calculation path according to vehicle sensor information and preset brake factors, so as to determine factors considered in subsequent taillight control, and determine a single-path remaining brake space corresponding to the preset calculation path according to the deceleration ratio, the deceleration increment ratio, and the road surface low adhesion ratio, so as to analyze respectively using different preset calculation paths, determine a fusion coefficient according to path reliability and current working condition complexity corresponding to each preset calculation path, so that the fusion coefficient is adapted to complex conditions of each preset calculation path and current scene, and further determine a fusion remaining brake space according to the fusion coefficient and the single-path remaining brake space corresponding to each preset calculation path, so as to adapt to different roads and weather conditions, control display brightness and display area of the vehicle taillight according to the fusion remaining brake space, thereby realizing variable dynamic control, adapting to complex road conditions, and providing more visual and more accurate rear-end light braking risk information, and reducing rear-end light traffic conditions.
Drawings
FIG. 1 is a flow chart of a method for controlling a brake taillight according to an embodiment of the present application;
FIG. 2 is a flowchart of another method for controlling a brake taillight according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The application is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting of the application. It should be noted that, for convenience of description, only the portions related to the application are shown in the drawings.
It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other. The application will be described in detail below with reference to the drawings in connection with embodiments.
As mentioned in the background art, the application provides a control method of a brake tail lamp, which is suitable for analyzing road condition environment and performing pre-judgment to rationalize and control the tail lamp, and provides visual and reliable visual warning for a rear vehicle in advance.
Fig. 1 is a flowchart of a method for controlling a brake taillight according to an embodiment of the present application. Referring to fig. 1, the brake taillight control method specifically includes:
S110, aiming at each preset calculation path, determining the deceleration ratio, the deceleration increment ratio and the pavement low adhesion ratio corresponding to the preset calculation path according to the vehicle sensor information and the preset braking factor.
The preset calculation path comprises a mathematical model path and a neural network model path. The mathematical model path is a path for data processing by using a traditional mathematical calculation mode, and the neural network model path is a path for data analysis processing by pre-constructing and training a neural network model. The vehicle sensor information is information collected by various sensors on the vehicle, and can comprise vehicle motion information such as wheel rotation speed, slip rate, brake pressure and the like. The preset braking factor is the data of experimental calibration, and is used for correcting the braking system, the tire condition and the like. The deceleration ratio is the ratio of the maximum available deceleration that the current deceleration has occupied. The deceleration delta ratio is the ratio of the current deceleration delta to the maximum available deceleration. The road surface low adhesion ratio is a value for describing dry or wet skid of the road surface. It will be appreciated that the deceleration duty cycle, the deceleration delta duty cycle, and the road surface low traction ratio are all between 0 and 1.
Specifically, each preset calculation path independently calculates the single-path residual braking space. When the mathematical model path calculation is used, the vehicle sensor information is used for calculating the deceleration ratio, the deceleration increment ratio and the pavement low adhesion ratio by a traditional mathematical method. When the neural network model path calculation is used, vehicle sensor information is input into a pre-established neural network model, and a deceleration ratio, a deceleration increment ratio and a road surface low adhesion ratio are output.
It can be understood that the mathematical model path is simple and efficient to calculate, but difficult to handle complex road conditions, and the neural network model path can handle complex road conditions, has higher sensitivity, but has poor interpretability.
S120, determining a single-path residual braking space corresponding to a preset calculation path according to the deceleration duty ratio, the deceleration increment duty ratio and the pavement low adhesion ratio.
The single-path residual braking space is the rear vehicle safety braking time calculated by each preset calculation path.
Specifically, for each preset calculation path, the deceleration ratio, the deceleration increment ratio, the road surface low adhesion ratio and the corresponding preset weights are used for weighted summation processing to obtain a used braking space, the used braking space is subtracted by the total braking space 1, and the difference is processed to be between 0 and 1, so that a single-path residual braking space corresponding to the preset calculation path can be obtained.
On the basis of the above example, the single-path remaining braking space corresponding to the preset calculation path may be determined according to the deceleration duty ratio, the deceleration increment duty ratio, and the road surface low adhesion ratio by:
Respectively weighting the deceleration ratio, the deceleration increment ratio and the road surface low adhesion ratio according to a first coefficient, a second coefficient and a third coefficient corresponding to a preset calculation path, and determining a weighted residual braking space;
If the weighted remaining braking space is greater than 1, determining that the single-path remaining braking space corresponding to the preset calculation path is 1;
if the weighted remaining braking space is greater than or equal to 0 and less than or equal to 1, taking the weighted remaining braking space as a single-path remaining braking space corresponding to a preset calculation path;
if the weighted remaining braking space is smaller than 0, determining that the single-path remaining braking space corresponding to the preset calculation path is 0.
The first coefficient, the second coefficient and the third coefficient are calibration values, and may be the same or different in different preset calculation paths. The weighted remaining braking space is the remaining braking space calculated by means of a weighted calculation, which may not be between 0 and 1.
Specifically, for each preset calculation path, a first coefficient, a second coefficient and a third coefficient corresponding to the preset calculation path are called, the deceleration duty ratio corresponding to the preset calculation path is weighted by using the first coefficient, the deceleration increment duty ratio corresponding to the preset calculation path is weighted by using the second coefficient, the road surface low adhesion ratio corresponding to the preset calculation path is weighted by using the third coefficient, the sum of the three coefficients is used as a used braking space, and the used braking space is subtracted by using 1, so that a weighted residual braking space is obtained. The weighted remaining braking space needs to be adjusted to be between 0 and 1, so that if the weighted remaining braking space is greater than 1, the single-path remaining braking space corresponding to the preset calculation path is determined to be 1, if the weighted remaining braking space is greater than or equal to 0 and less than or equal to 1, the weighted remaining braking space is taken as the single-path remaining braking space corresponding to the preset calculation path, and if the weighted remaining braking space is less than 0, the single-path remaining braking space corresponding to the preset calculation path is determined to be 0.
Illustratively, the first, second, and third coefficients reflect sensitivity to deceleration duty cycle, deceleration delta duty cycle, and road surface low adhesion ratio, respectively. In a mathematical model path, a first coefficient is weight corresponding to the deceleration ratio, and the calibration thought is that a plurality of short-time sudden braking data are collected in a test field, when the deceleration is observed to be large, the expected warning requirement of a rear vehicle is met, if the rear vehicle is more vigilant to 'current deceleration is large', the first coefficient is increased, meanwhile, the first coefficient is considered, if the first coefficient is too large, the single-path residual braking space is obviously reduced due to the fact that a brake is possibly stepped on lightly, the tail lamp is obviously changed, and therefore, the numerical value for balancing the sensitivity and the stability is generally taken, for example, the empirical range is 1.0-3.0 (different choices can be made according to the dimension and the braking characteristic of the whole vehicle). The second coefficient is the weight corresponding to the deceleration increment ratio, the calibration thinking is that the test working condition is specially designed, namely, the initial stage of lightly stepping on the brake (the deceleration is smaller), then the brake is rapidly stepped deeply (the deceleration is rapid), the feedback or simulation of a rear vehicle driver confirms under what abrupt threshold, a stronger prompt is needed according to the expression of the deceleration increment, for example, the experience range is 0.5-2.0, and if the small vibration is to be restrained, the first-order filtering or hysteresis processing can be matched. The third coefficient is a weight corresponding to a low adhesion ratio of the road surface, and the calibration thinking is that the test field simulates a slippery ground or a snowfield to brake and observe the real following safety, if the road condition changes rapidly (for example, a road section just entering water accumulation), a slightly higher third coefficient can be set, and risk prompts caused by low adhesion are amplified in advance, for example, the experience range is 0.2-1.0, and the condition that the third coefficient is too large to cause a high warning state under the common wetland working condition is avoided. The path calculation of the mathematical model is simple and efficient, the parameters are visual and interpretable, and engineering realization and fault diagnosis are easy. Corresponding first coefficients, second coefficients and third coefficients can be dynamically generated in the neural network model through a reinforcement learning algorithm. The neural network model path can adaptively adjust the weight, and the residual braking space evaluation is optimized according to different scenes, so that the complex edge situation is more robust to process.
The single-path remaining braking space corresponding to each preset calculation path is determined by the following formula:
Rtrad(t) = clamp(1-λ1*r1trad(t)-λ2*r2trad(t)-λ3*r3trad(t), 0, 1)
RAI(t) = clamp(1-w1(t)*r1AI(t)-w2(t)*r2AI(t)-w3(t)*r3AI(t), 0, 1)
Wherein, R trad (t) is a single-path residual braking space corresponding to a mathematical model path, R AI (t) is a single-path residual braking space corresponding to a neural network model path, lambda 1、λ2 and lambda 3 are first, second and third coefficients corresponding to a neural network model path, w 1、w2 and w 3 are first, second and third coefficients corresponding to a neural network model path, R 1trad (t) is a deceleration duty cycle corresponding to a mathematical model path, R 2trad (t) is a deceleration increment duty cycle corresponding to a mathematical model path, R 3trad (t) is a road surface low duty cycle corresponding to a mathematical model path, R 1AI (t) is a deceleration duty cycle corresponding to a neural network model path, R 2AI (t) is a deceleration increment duty cycle corresponding to a neural network model path, R 3AI (t) is a road surface low duty cycle corresponding to a neural network model path, and clamp (x, 0, 1) represents that x is defined within a section [0,1], clx <0, 1=0, 1.
S130, determining a fusion coefficient according to the path reliability and the current working condition complexity corresponding to each preset calculation path, and determining a fusion residual braking space according to the fusion coefficient and the single-path residual braking space corresponding to each preset calculation path.
The path reliability is the reliability of the preset calculation path when the path is used, and can be understood as the confidence when the path is used. The current operating condition complexity is a description of the operating condition determined in consideration of the current driving environment of the vehicle and the moving state of the vehicle. The fusion coefficient is a coefficient for fusing the single-path residual braking space corresponding to the two preset calculation paths. The fused residual braking space is a numerical value obtained by fusing the single-path residual braking spaces corresponding to the two preset calculation paths, and is also a basis for controlling the tail lamp of the vehicle.
Specifically, the path reliability corresponding to each preset calculation path is obtained, the current working condition complexity is obtained, the first fusion part can be determined by combining the path reliability corresponding to each preset calculation path, the second fusion part can be determined by analyzing the current working condition complexity, and the fusion coefficient can be determined by combining the first fusion part and the second fusion part in a self-adaptive manner. And carrying out data fusion on the single-path residual braking space corresponding to each preset calculation path by using the fusion coefficient to obtain a fused residual braking space.
Based on the above example, before determining the fusion coefficient according to the path reliability and the current working condition complexity corresponding to each preset calculation path, the current working condition complexity may be further calculated, and the determination of the fusion coefficient may specifically be:
determining the change rate of the road surface attachment coefficient according to the road surface attachment coefficient at the current moment and the road surface attachment coefficient at the previous moment corresponding to the mathematical model path;
determining the environment complexity according to each environment factor;
And determining the complexity of the current working condition according to the deceleration ratio, the road surface adhesion coefficient change rate and the environment complexity corresponding to the mathematical model path.
The road adhesion coefficient is data for describing wet or dry road. The road surface adhesion coefficient change rate is used for describing the change speed of the road surface adhesion coefficient, namely the road condition change speed. Environmental factors may include weather factors, light and shade factors, temperature factors, and the like. Environmental complexity is used to describe the complexity of the current vehicle surroundings.
Specifically, the road surface adhesion coefficient at the current moment and the road surface adhesion coefficient at the previous moment corresponding to the mathematical model path are obtained, and the ratio of the difference value of the road surface adhesion coefficient and the road surface adhesion coefficient to the time interval is calculated to obtain the change rate of the road surface adhesion coefficient. And (5) analyzing by integrating all the environmental factors to obtain the environmental complexity. And comparing the deceleration ratio, the road surface adhesion coefficient change rate and the environment complexity corresponding to the mathematical model path, and taking the maximum value as the complexity of the current working condition.
By way of example, the current operating condition complexity may be determined by:
Ccomp(t) = max(
k1·normalize(|dμtrad(t)/dt|),
k2·normalize(|a(t)|/aavailtrad(t)),
k3·normalize(envComplexity)
)
Wherein Ccomp (t) is the current operating condition complexity, k 1、k2 and k 3 are the road attachment coefficient change rate, the deceleration ratio corresponding to the mathematical model path, and the weight coefficient corresponding to the environmental complexity, respectively, |dμ trad (t)/dt|is the road attachment coefficient change rate corresponding to the mathematical model path, |a (t) |/a availtrad (t) is the deceleration ratio corresponding to the mathematical model path, envComplexity is the environmental complexity, max () is the maximum value, normalize () is normalized to the [0,1] interval.
By way of example, the environmental complexity may be calculated by:
envComplexity=env1·isRaining+env2·isSnowing+env3·(1-normalizedTemp) +env4·isNight
The same representation as the above example is not repeated, ISRAINING is a binary representation of whether to rain, isSnowing is a binary representation of whether to snow, isNight is a binary representation of whether to night, and it is understood that if yes, the binary representation is 1, and if not, the binary representation is 0.normalizedTemp is a normalized value of the current temperature, env 1、env2、env3 and env 4 are importance coefficients corresponding to various indexes, for example, env 1=0.5,env2=0.7,env3 =0.3, =0.4, and the specific values can be adjusted according to actual requirements, and are not limited herein.
Based on the above example, before determining the fusion coefficient according to the path reliability corresponding to each preset calculation path and the complexity of the current working condition, the path reliability corresponding to the path of the mathematical model and the path reliability corresponding to the path of the neural network model may be further determined, which may specifically be:
acquiring an electronic control unit health degree and a sensor data quality evaluation value of a vehicle;
Determining the path reliability corresponding to the path of the mathematical model according to the health degree of the electronic control unit and the sensor data quality evaluation value;
Obtaining a result prediction difference value of a target model and a preset evaluation model in a neural network model path and data quality of vehicle sensor information;
determining a model confidence coefficient corresponding to the neural network model path according to the result prediction difference value and the data quality;
And determining the path reliability corresponding to the neural network model path according to the health degree of the electronic control unit, the sensor data quality evaluation value and the model confidence degree.
Wherein, the health of the electronic control unit is an index for measuring the hardware state of the vehicle, and the numerical range is 0, 1. The sensor data quality evaluation value is an index for measuring the sensor data quality, and is related to the signal-to-noise ratio and the data stability, and the numerical range is 0, 1. The target model is a neural network model used in a neural network model path. The preset evaluation model is a model for comparing an output result with a target model, and has a different model structure from the target model. The result prediction difference is a difference between an output result of the target model based on the vehicle sensor information and an output result of the preset evaluation model based on the vehicle sensor information. The data quality is the suitability between the vehicle sensor information and the training data used by the training target model for describing the reliability of the vehicle sensor information.
Specifically, an electronic control unit health degree of the vehicle and a sensor data quality evaluation value are obtained. And combining the health degree of the electronic control unit and the sensor data quality evaluation value, the path reliability corresponding to the path of the mathematical model can be calculated and obtained. Inputting the vehicle sensor information into a target model in a neural network model path to obtain a first result, inputting a preset evaluation model to obtain a second result, taking the difference value of the first result and the second result as a result prediction difference value, and evaluating the vehicle sensor information by using a data quality evaluation model to obtain data quality. Wherein the data quality assessment model is a model that is pre-trained using each valid input data and invalid input data. And comprehensively analyzing the result prediction difference and the data quality, and determining the model confidence coefficient corresponding to the neural network model path, for example, normalizing the result prediction difference and the data quality, and solving the maximum value of the 1-minus-normalized result prediction difference and the normalized data quality to obtain the model confidence coefficient corresponding to the neural network model path. And comprehensively analyzing the health degree of the electronic control unit, the sensor data quality evaluation value and the model confidence degree, and calculating to obtain the path reliability corresponding to the neural network model path.
By way of example, the path reliability corresponding to the mathematical model path and the path reliability corresponding to the neural network model path may be calculated by:
Creltrad(t)=whwtrad*hwStatus+wsenstrad*sensorQuality
CrelAI(t)=whwAI*hwStatus+wsensAI*sensorQuality+wconfAI*AIconfidence
Wherein, crel trad (t) is the path reliability corresponding to the path of the mathematical model, crel AI (t) is the path reliability corresponding to the path of the neural network model, hwStatus is the health of the electronic control unit, sensorQuality is the sensor data quality evaluation value, AIconfidence is the model confidence corresponding to the path of the neural network model, w hwtrad and w senstrad are the weight coefficients corresponding to the path of the mathematical model, w hwAI、wsensAI and w confAI are the weight coefficients corresponding to the path of the neural network model, and the sum of the weight coefficients corresponding to the path is 1.
And S140, controlling the display brightness and the display area of the tail lamp of the vehicle according to the fusion of the residual braking space.
The display luminance is the brightness of the vehicle tail lamp, and the display area is the area where the vehicle tail lamp is lighted.
Specifically, the fused remaining braking space is converted into a percentage, the display brightness and the display area corresponding to the percentage are determined, the vehicle tail lamp is controlled to be lightened according to the determined display brightness and display area, and it is understood that the signal for controlling the vehicle tail lamp CAN be a PWM (Pulse Width Modulation ) signal or a CAN (Controller Area Network, controller area network bus) signal.
On the basis of the above example, the display brightness and the display area of the vehicle tail lamp can be controlled according to the fusion of the remaining braking space in the following manner:
If the fused residual braking space is larger than or equal to a first threshold value, determining that the display brightness of the vehicle tail lamp is the preset minimum brightness, and determining that the display area of the vehicle tail lamp is the preset minimum area;
If the fused residual braking space is smaller than the first threshold value and larger than or equal to the second threshold value, determining a target proportion according to the fused residual braking space, the first threshold value and the second threshold value, determining the display brightness of the vehicle tail lamp according to the target proportion, the preset minimum brightness and the preset maximum brightness, and determining the display area of the vehicle tail lamp according to the target proportion, the preset minimum area and the preset maximum area;
If the fused residual braking space is smaller than the second threshold value, determining that the display brightness of the vehicle tail lamp is the preset highest brightness, and determining that the display area of the vehicle tail lamp is the preset largest area.
The first threshold and the second threshold are preset percentage values for dividing different grades, the first threshold is larger than the second threshold, for example, the first threshold is 70%, the second threshold is 30%, and the like, and specific values can be calibrated according to actual conditions. The preset maximum luminance and the preset minimum luminance are the highest luminance and the lowest luminance that can be displayed by the vehicle tail lamp specified by law. The preset maximum area and the preset minimum area are the maximum area and the minimum area that can be displayed by the vehicle tail lamp specified by the law. The target ratio is used to describe the position of the fusion braking space between the first and second thresholds, such as the ratio between the difference of the fusion braking space and the second threshold and the difference of the first and second thresholds, etc.
Specifically, if the fused residual braking space is greater than or equal to the first threshold, the residual safe braking time of the rear vehicle is indicated to be longer, the display brightness of the rear vehicle lamp can be determined to be the preset minimum brightness, and the display area of the rear vehicle lamp is determined to be the preset minimum area. If the fused residual braking space is smaller than the first threshold value and larger than or equal to the second threshold value, the safety braking time reserved for the rear vehicle is limited, but the method is not particularly urgent, the display brightness and the display area can be dynamically adjusted according to the fused residual braking space, the ratio between the difference value between the fused braking space and the second threshold value and the difference value between the first threshold value and the second threshold value is taken as a target proportion, the display brightness of the rear vehicle lamp is linearly determined between the preset minimum brightness and the preset maximum brightness according to the target proportion, and the display area of the rear vehicle lamp is linearly determined between the preset minimum area and the preset maximum area according to the target proportion. And if the fused residual braking space is smaller than the second threshold value, the vehicle is indicated to be braked urgently, so that the display brightness of the vehicle tail lamp is determined to be the preset highest brightness, and the display area of the vehicle tail lamp is determined to be the preset largest area.
According to the brake tail lamp control method provided by the embodiment of the application, the deceleration ratio, the deceleration increment ratio and the pavement low adhesion ratio corresponding to the preset calculation paths are determined according to the vehicle sensor information and the preset brake factors for each preset calculation path, so that the factors considered in the follow-up tail lamp control process are conveniently determined, the single-path residual brake space corresponding to the preset calculation paths is determined according to the deceleration ratio, the deceleration increment ratio and the pavement low adhesion ratio, so that analysis is conveniently carried out respectively by using different preset calculation paths, the fusion coefficient is determined according to the path reliability and the current working condition complexity corresponding to each preset calculation path, so that the fusion coefficient is adapted to the complex conditions of each preset calculation path and the current scene, and further, the fusion residual brake space is determined according to the fusion coefficient and the single-path residual brake space corresponding to each preset calculation path, so that the display brightness and the display area of the vehicle tail lamp are controlled according to the fusion residual brake space through the dual-path design, the dynamic control of the rear-end lamp is realized, the complex environment is adapted to the complex vehicle, the visual and the weather information is provided for the rear-end collision risk is reduced, and the weather information is more visual.
Fig. 2 is a flowchart of another control method for a brake taillight according to an embodiment of the present application. On the basis of the above embodiments, the calculation process of the deceleration ratio, the deceleration increment ratio, and the road surface low adhesion ratio, the analysis process of the fusion coefficient, and the display control process of the vehicle tail lamp have been exemplified. Referring to fig. 2, the brake taillight control method specifically includes:
S210, determining road surface adhesion coefficients according to vehicle sensor information based on the preset calculation paths and determining road surface low adhesion ratio corresponding to the preset calculation paths according to the road surface adhesion coefficients.
Specifically, the road surface adhesion coefficient mu trad (t) can be estimated on line by analyzing the slip ratio of wheels in the sensor information of the vehicle based on the path of the mathematical model, and the road surface low adhesion ratio is obtained by subtracting the road surface adhesion coefficient from 1. The vehicle sensor information is input into a neural network model in a neural network model path, a road surface adhesion coefficient mu AI (t) is output, and the road surface adhesion coefficient is subtracted by 1 to obtain a road surface low adhesion ratio.
For example, the neural network model in the neural network model path may be expressed as μai (t) =g (S (t), θ), where the network input is a multidimensional feature vector S (t), that is, vehicle sensor information such as a wheel speed, a slip ratio, a brake pressure, etc., and θ is a vehicle steering angle. The network structure comprises an input layer, a first hidden layer, a second hidden layer and an output layer, wherein the input layer receives S (t) and theta to obtain 8-12 characteristics, the first hidden layer is a fully-connected layer and a ReLU consisting of 16-32 units, the second hidden layer is a fully-connected layer and a ReLU consisting of 8-16 units, and the output layer can be a Sigmoid function or a normalization function so that mu AI (t) is E [0,1]. It can be understood that the neural network model can be trained offline through multi-path condition data and fine-tuned online, and can process complex road conditions, capture nonlinear relations which are difficult to identify by mathematical model paths, and have higher sensitivity to abnormal road conditions (such as partial icing).
S220, determining the maximum available deceleration corresponding to the preset calculation path according to the road adhesion coefficient, the preset braking factor and the gravity acceleration, and determining the deceleration change rate according to the actual deceleration at the current moment and the actual deceleration at the previous moment.
The preset braking factor is a calibration value considering a braking system, a tire condition and the like, and is ideally 1, and is smaller than 1 under the conditions of tire wear, overhigh temperature or braking system attenuation. The actual deceleration is a deceleration value measured by a sensor, and may be a value obtained by low-pass filtering to suppress noise. The maximum available deceleration is the maximum deceleration that can be achieved before the vehicle, and is a positive number.
Specifically, for each preset calculation path, taking the product of the road surface attachment coefficient, the preset braking factor and the gravity acceleration corresponding to the preset calculation path as the maximum available deceleration corresponding to the preset calculation path. The ratio of the time interval to the difference between the actual deceleration at the present time and the actual deceleration at the preceding time is used as the deceleration rate.
Illustratively, the maximum available deceleration for each preset calculation path is calculated by the following formula:
aavailtrad(t)=μtrad(t)*g*k
aavailAI(t)=μAI(t)*g*k
Wherein a availtrad (t) is the maximum available deceleration corresponding to the mathematical model path, μ trad (t) is the road surface attachment coefficient corresponding to the mathematical model path, a availAI (t) is the maximum available deceleration corresponding to the neural network model path, μ AI (t) is the road surface attachment coefficient corresponding to the neural network model path, g is the gravitational acceleration, and k is the preset braking factor.
The deceleration change rate is calculated by the following formula, and it can be understood that the deceleration change rates corresponding to the two preset calculation paths are the same:
da(t)/dt=(a(t)-a(t-1))/Ts
Where da (T)/dt is the deceleration rate, a (T) is the actual deceleration at the current time, a (T-1) is the actual deceleration at the previous time, and T s is the time interval.
S230, determining a deceleration ratio corresponding to the preset calculation path according to the actual deceleration at the current moment and the maximum available deceleration corresponding to the preset calculation path, and determining a deceleration increment ratio corresponding to the preset calculation path according to the deceleration change rate and the maximum available deceleration corresponding to the preset calculation path.
Specifically, for each preset calculation path, dividing the absolute value of the actual deceleration at the current moment by the maximum available deceleration corresponding to the preset calculation path to obtain the deceleration duty ratio corresponding to the preset calculation path, and dividing the deceleration change rate by the maximum available deceleration corresponding to the preset calculation path to obtain the deceleration increment duty ratio corresponding to the preset calculation path.
For example, the deceleration ratio and the deceleration increment ratio corresponding to each preset calculation path are calculated by the following formula:
r1trad(t) = |a(t)| / aavailtrad(t)
r2trad(t) = |da(t)/dt| / aavailtrad(t)
r1AI(t) = |a(t)| / aavailAI(t)
r2AI(t) = |da(t)/dt| / aavailAI(t)
The same representation as the above example is not repeated, where r 1trad (t) is a deceleration duty ratio corresponding to the mathematical model path, r 2trad (t) is a deceleration increment duty ratio corresponding to the mathematical model path, r 1AI (t) is a deceleration duty ratio corresponding to the neural network model path, and r 2AI (t) is a deceleration increment duty ratio corresponding to the neural network model path.
S240, determining a single-path residual braking space corresponding to the preset calculation path according to the deceleration duty ratio, the deceleration increment duty ratio and the pavement low adhesion ratio.
S250, determining a reliability difference value according to the path reliability corresponding to the mathematical model path and the path reliability of the neural network model path, determining a first addend according to the reliability difference value and the preset reliability difference sensitivity, and determining a second addend according to the complexity of the current working condition, the preset complexity and the preset complexity sensitivity.
The reliability difference value is the difference value between the path reliability corresponding to the mathematical model path and the neural network model path. The preset complexity is a preset complexity value for judging the difficulty level, and can be generally set to 0.5 or can be adjusted according to the requirement. The first and second summands are intermediate values in the calculation process. The preset reliability difference sensitivity and the preset complexity sensitivity are preset values for describing the reliability difference and the complexity importance of the current working condition, for example, the reliability difference sensitivity is 5.0, the preset complexity sensitivity is 3.0, and the like.
Specifically, a difference value between the path reliability corresponding to the mathematical model path and the path reliability of the neural network model path is calculated, and the product of the reliability difference value and the preset reliability difference sensitivity is determined as a first addend. And determining the product of the difference value of the preset complexity and the complexity of the current working condition and the sensitivity of the preset complexity as a second addition number.
S260, processing the sum value of the first addend and the second addend through a preset regression function to obtain a fusion coefficient.
The preset regression function is a preset regression function, and may be a sigmoid function or the like.
Specifically, the first addend and the second addend are subjected to summation treatment, and the obtained sum value is substituted into a preset regression function to obtain a fusion coefficient.
Illustratively, the fusion coefficient may be calculated by the following formula:
α(t) = sigmoid(τ1*( Creltrad(t) - CrelAI(t)) + τ2*(C - Ccomp(t)))
Where α (t) is a fusion coefficient, crel trad (t) is a path reliability corresponding to a mathematical model path, crel AI (t) is a path reliability corresponding to a neural network model path, ccomp (t) is a current working condition complexity, C is a preset complexity, τ 1 is a preset reliability difference sensitivity, τ 2 is a preset complexity sensitivity, sigmoid (x) =1/(1+e (-x)), and the result is mapped to the [0,1] interval.
It can be understood that taking the preset complexity of 0.5 as an example, when the mathematical model path is more reliable and the scene is simple, α (t) approaches 1, which represents a biased mathematical model path, when the neural network model path is more reliable and the scene is complex, α (t) approaches 0, which represents a biased neural network model path, and when the reliability of the two preset calculation paths is equivalent and the complexity is moderate, α (t) approaches 0.5, which represents that the two preset calculation paths can be uniformly fused.
S270, determining a fusion residual braking space according to the fusion coefficient and the single-path residual braking space corresponding to each preset calculation path.
Illustratively, the fused remaining braking space may be determined by:
Rfinal(t) = α(t)·Rtrad(t) + (1-α(t))·RAI(t)
Wherein R final (t) is a fusion braking space, R trad (t) is a single-path residual braking space corresponding to a mathematical model path, R AI (t) is a single-path residual braking space corresponding to a neural network model path, and alpha (t) is a fusion coefficient.
S280, determining the target residual braking space at the current moment according to the preset filter coefficient, the fused residual braking space and the target residual braking space at the previous moment, and updating the target residual braking space at the current moment based on a hysteresis mechanism.
The preset filter coefficient is a coefficient preset for performing filter processing. The target residual braking space is the residual braking space after the fusion residual braking space is subjected to filtering processing and hysteresis mechanism processing.
Specifically, based on a preset filter coefficient and a target residual braking space at the previous moment, processing the fused residual braking space to obtain a target residual braking space at the current moment, processing the processed target residual braking space at the current moment by using a hysteresis mechanism, updating the target residual braking space at the current moment, introducing the hysteresis mechanism, and preventing oscillation near a threshold value.
Illustratively, the fused residual braking space is filtered based on the following formula:
Rfilteredfinal(t) = β·Rfilteredfinal(t-1) + (1-β)·Rfinal(t)
Wherein, beta is a preset filter coefficient, usually 0.3-0.7 is taken, rfiltered final (t) is the target residual braking space at the current moment, RFILTERED final (t-1) is the target residual braking space at the previous moment, and R final is the fused residual braking space.
And S290, controlling the display brightness and the display area of the tail lamp of the vehicle according to the updated target residual braking space at the current moment.
Specifically, similar to S140, the updated target remaining braking space at the current time may be used to perform display control on the vehicle tail lamp instead of the fused remaining braking space.
According to the brake taillight control method provided by the embodiment of the application, the road surface attachment coefficient is determined according to the vehicle sensor information based on the preset calculation paths for each preset calculation path, the road surface low attachment ratio corresponding to the preset calculation paths is determined according to the road surface attachment coefficient, and the maximum available deceleration corresponding to the preset calculation paths is determined according to the road surface attachment coefficient, the preset braking factor and the gravity acceleration; determining a deceleration change rate according to the actual deceleration at the current moment and the actual deceleration at the previous moment, determining a deceleration duty ratio corresponding to a preset calculation path according to the actual deceleration at the current moment and the maximum available deceleration corresponding to the preset calculation path, determining a deceleration increment duty ratio corresponding to the preset calculation path according to the deceleration change rate and the maximum available deceleration corresponding to the preset calculation path, calculating the required basic information of each preset calculation path more accurately, further, determining a reliability difference value according to the path reliability corresponding to a mathematical model path and the path reliability of a neural network model path, determining a first addend according to the reliability difference value and the preset reliability difference sensitivity, determining a second addend according to the complexity of the current working condition, the preset complexity and the preset complexity sensitivity, processing the sum value of the first addend and the second addend through a preset regression function to obtain a fusion coefficient, fully considering the reliability of each preset calculation path and the current working condition complexity to flexibly determine the fusion coefficient, and determining a residual brake space based on a residual brake space time and a residual brake space based on a residual brake space mechanism, the method has the advantages that the target residual braking space at the current moment is updated to improve the stability and effectiveness of the target residual braking space, high-precision real-time evaluation of the residual braking space and dynamic control of the tail lamp are realized, the method can adapt to complex and changeable road condition environments, the method has prejudgement capability on road adhesion coefficient changes, visual and reliable visual warning is provided for a rear vehicle in advance, the self-adaptive fusion mechanism ensures that the balance of stability and sensitivity is kept under various scenes, the rear-end collision risk is effectively reduced through optimizing the display strategy of the tail lamp, and the road driving safety is improved.
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application. As shown in fig. 3, the electronic device 300 includes one or more processors 301 and memory 302.
The processor 301 may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities and may control other components in the electronic device 300 to perform desired functions.
Memory 302 may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, random Access Memory (RAM) and/or cache memory (cache), and the like. The non-volatile memory may include, for example, read Only Memory (ROM), hard disk, flash memory, and the like. One or more computer program instructions may be stored on the computer readable storage medium that may be executed by the processor 301 to implement the brake taillight control method and/or other desired functions of any of the embodiments of the present application described above. Various content such as initial arguments, thresholds, etc. may also be stored in the computer readable storage medium.
In one example, electronic device 300 may also include input device 303 and output device 304, which may be interconnected by a bus system and/or other form of connection mechanism (not shown). The input device 303 may include, for example, a keyboard, a mouse, and the like. The output device 304 can output various information to the outside, including early warning prompt information, braking force, etc. The output device 304 may include, for example, a display, speakers, a printer, and a communication network and remote output devices connected thereto, etc.
Of course, only some of the components of the electronic device 300 that are relevant to the present application are shown in fig. 3 for simplicity, components such as buses, input/output interfaces, etc. are omitted. In addition, the electronic device 300 may include any other suitable components depending on the particular application.
In addition to the methods and apparatus described above, embodiments of the present application may also be a computer program product comprising computer program instructions which, when executed by a processor, cause the processor to perform the steps of the brake taillight control method provided by any of the embodiments of the present application.
The computer program product may write program code for performing operations of embodiments of the present application in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server.
Furthermore, embodiments of the present application may also be a computer-readable storage medium, on which computer program instructions are stored, which, when being executed by a processor, cause the processor to perform the steps of the brake taillight control method provided by any of the embodiments of the present application.
The computer readable storage medium may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may include, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of a readable storage medium include an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of the present application. As used in the specification and in the claims, the terms "a," "an," "the," and/or "the" are not specific to a singular, but may include a plurality, unless the context clearly dictates otherwise. The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, or apparatus. Without further limitation, an element defined by the phrase "comprising one does not exclude the presence of other like elements in a process, method or apparatus comprising such elements.
It should also be noted that the positional or positional relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. are based on the positional or positional relationship shown in the drawings, are merely for convenience of describing the present application and simplifying the description, and do not indicate or imply that the apparatus or element in question must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present application. Unless specifically stated or limited otherwise, the terms "mounted," "connected," and the like should be construed broadly, and may, for example, be fixedly connected, detachably connected, or integrally connected, mechanically connected, electrically connected, directly connected, or indirectly connected through an intermediary, or may be in communication with the interior of two elements. The specific meaning of the above terms in the present application will be understood in specific cases by those of ordinary skill in the art.
The principles and embodiments of the present application have been described herein with reference to specific examples, the description of which is intended only to facilitate an understanding of the method of the present application and its core ideas. The foregoing is merely illustrative of the preferred embodiments of the application, and it will be appreciated that numerous modifications, adaptations and variations of the application can be made by those skilled in the art without departing from the principles of the application, and that other features and advantages of the application can be combined in any suitable manner, and that no improvement in the design or design of the application is intended to be applied directly to other applications.
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