Brake tail light control method, electronic device and medium

Abstract

The present application provides a brake taillight control method, electronic device, and medium. The method includes: for each preset calculation path, determining the deceleration ratio, deceleration increment ratio, and road surface low adhesion ratio corresponding to the preset calculation path based on vehicle sensor information and a preset braking factor; determining the single-path remaining braking space corresponding to the preset calculation path based on the deceleration ratio, deceleration increment ratio, and road surface low adhesion ratio; determining a fusion coefficient based on the path reliability corresponding to each preset calculation path and the complexity of the current working condition, and determining the fused remaining braking space based on the fusion coefficient and the single-path remaining braking space corresponding to each preset calculation path; and controlling the display brightness and display area of the vehicle taillights based on the fused remaining braking space. Through the technical solution of the present application, dynamic control of the taillights is achieved, adapting to complex and changing road conditions, and providing intuitive and reliable visual warnings for following vehicles.

Description

Technical Field

[0001] The present application relates to the field of vehicle control technology, and in particular to a brake taillight control method, electronic device, and medium. Background Art

[0002] Currently, the display control of vehicle brake taillights mostly adopts a fixed brightness or simple flashing method, which cannot fully inform the following vehicles of the actual deceleration of the leading vehicle and the current road conditions.

[0003] In addition, the control of the vehicle's brake taillights can also be triggered according to the brake pedal opening signal. This method is difficult to reflect factors such as slippery roads or sudden changes in emergency braking, which may cause the following vehicle to lag in judging the deceleration of the front vehicle, easily leading to rear-end collisions. Summary of the Invention

[0004] In view of the above-mentioned defects or deficiencies in the prior art, the present application aims to provide a brake taillight control method, electronic device and medium to achieve dynamic control of the taillights, adapt to complex and changing road conditions, and provide intuitive and reliable visual warnings for following vehicles.

[0005] The present application provides a brake tail light control method, including:

[0006] For each preset calculation path, determining the deceleration ratio, deceleration increment ratio, and road surface low adhesion ratio corresponding to the preset calculation path based on vehicle sensor information and a preset braking factor;

[0007] determining a single-path remaining braking space corresponding to the preset calculation path according to the deceleration ratio, the deceleration increment ratio, and the road surface low adhesion ratio;

[0008] Determine a fusion coefficient based on the path reliability corresponding to each preset calculation path and the complexity of the current working condition, and determine a fusion residual braking space based on the fusion coefficient and the single-path residual braking space corresponding to each preset calculation path;

[0009] Controlling the display brightness and display area of the vehicle taillights according to the fused remaining braking space;

[0010] The preset calculation path includes a mathematical model path and a neural network model path.

[0011] According to the technical solution provided in the embodiment of the present application, optionally, determining the deceleration ratio, deceleration increment ratio, and road surface low adhesion ratio corresponding to the preset calculation path based on vehicle sensor information and a preset braking factor includes:

[0012] Based on the preset calculation path, determining a road adhesion coefficient according to vehicle sensor information, and determining a road low adhesion ratio corresponding to the preset calculation path according to the road adhesion coefficient;

[0013] determining a maximum available deceleration corresponding to the preset calculation path according to the road adhesion coefficient, a preset braking factor, and gravity acceleration;

[0014] Determine the deceleration change rate based on the actual deceleration at the current moment and the actual deceleration at the previous moment;

[0015] 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;

[0016] The deceleration increment ratio corresponding to the preset calculation path is determined according to the deceleration change rate and the maximum available deceleration corresponding to the preset calculation path.

[0017] According to the technical solution provided in the embodiment of the present 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 includes:

[0018] determining a road surface adhesion coefficient change rate based on a road surface adhesion coefficient at a current moment and a road surface adhesion coefficient at a previous moment corresponding to the mathematical model path;

[0019] Determine the environmental complexity based on various environmental factors;

[0020] The complexity of the current working condition is determined according to the deceleration ratio corresponding to the mathematical model path, the road adhesion coefficient change rate and the environmental complexity.

[0021] According to the technical solution provided in an embodiment of the present application, optionally, determining the single-path remaining braking space corresponding to the preset calculation path based on the deceleration ratio, the deceleration increment ratio, and the road surface low adhesion ratio includes:

[0022] 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 the preset calculation path, respectively, to determine a weighted remaining braking space;

[0023] 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;

[0024] 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 used as the single-path remaining braking space corresponding to the preset calculation path;

[0025] If the weighted remaining braking space is less than 0, it is determined that the single-path remaining braking space corresponding to the preset calculation path is 0.

[0026] According to the technical solution provided in the embodiment of the present 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:

[0027] Determining a reliability difference 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 and a preset reliability difference sensitivity;

[0028] Determining a second addend according to the current operating condition complexity, the preset complexity, and the preset complexity sensitivity;

[0029] The sum of the first addend and the second addend is processed by a preset regression function to obtain a fusion coefficient.

[0030] According to the technical solution provided in the embodiments of this application,

[0031] 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 includes:

[0032] Obtain the vehicle's electronic control unit health and sensor data quality assessment values;

[0033] determining a path reliability corresponding to the mathematical model path according to the health of the electronic control unit and the sensor data quality assessment value;

[0034] Obtaining a prediction difference between a target model in the neural network model path and a preset evaluation model and data quality of the vehicle sensor information;

[0035] Determining the model confidence corresponding to the neural network model path based on the result prediction difference and the data quality;

[0036] The path reliability corresponding to the neural network model path is determined according to the health of the electronic control unit, the sensor data quality assessment value and the model confidence.

[0037] According to the technical solution provided in the embodiment of the present application, optionally, controlling the display brightness and display area of the vehicle taillights according to the integrated remaining braking space includes:

[0038] If the fused remaining braking space is greater than or equal to a first threshold, determining that the display brightness of the vehicle taillights is a preset minimum brightness, and determining that the display area of the vehicle taillights is a preset minimum area;

[0039] If the fused remaining braking space is less than the first threshold and greater than or equal to the second threshold, determining a target ratio based on the fused remaining braking space, the first threshold, and the second threshold, determining a display brightness of the vehicle taillights based on the target ratio, the preset minimum brightness, and the preset maximum brightness, and determining a display area of the vehicle taillights based on the target ratio, the preset minimum area, and the preset maximum area;

[0040] If the fused remaining braking space is less than the second threshold, the display brightness of the vehicle taillights is determined to be the preset maximum brightness, and the display area of the vehicle taillights is determined to be the preset maximum area.

[0041] According to the technical solution provided in the embodiment of the present application, optionally, controlling the display brightness and display area of the vehicle taillights according to the integrated remaining braking space includes:

[0042] determining the target remaining braking space at the current moment according to a preset filter coefficient, the fused remaining braking space, and the target remaining braking space at the previous moment;

[0043] Based on the hysteresis mechanism, updating the target remaining braking space at the current moment;

[0044] The display brightness and display area of the vehicle taillights are controlled according to the updated target remaining braking space at the current moment.

[0045] An embodiment of the present application further provides an electronic device, comprising:

[0046] processor and memory;

[0047] The processor is configured to execute the steps of the brake tail light control method as described in any embodiment by calling the program or instructions stored in the memory.

[0048] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a program or instruction, wherein the program or instruction enables a computer to execute the steps of the brake taillight control method as described in any embodiment.

[0049] In summary, the present application proposes a brake taillight control method. For each preset calculation path, based on vehicle sensor information and a preset braking factor, the deceleration percentage, deceleration increment percentage, and road surface low-adhesion ratio corresponding to the preset calculation path are determined to facilitate determining factors for consideration in subsequent taillight control. Based on the deceleration percentage, deceleration increment percentage, and road surface low-adhesion ratio, the single-path remaining braking space corresponding to the preset calculation path is determined, facilitating separate analysis using different preset calculation paths. A fusion coefficient is determined based on the path reliability corresponding to each preset calculation path and the complexity of the current operating conditions, so that the fusion coefficient is adapted to the complexity of each preset calculation path and the current scenario. Furthermore, a fused remaining braking space is determined based on the fusion coefficient and the single-path remaining braking space corresponding to each preset calculation path. This dual-path design adapts to different road and weather conditions. Based on the fused remaining braking space, the display brightness and display area of the vehicle's taillights are controlled, achieving dynamic control of the taillights, adapting to complex and changing road conditions, providing more intuitive and accurate braking information to following vehicles, and reducing the risk of rear-end collisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a flow chart of a brake taillight control method provided by an embodiment of the present application;

[0051] Figure 2 is a flow chart of another brake tail light control method provided by an embodiment of the present application;

[0052] Figure 3 This is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0053] The present application will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely for the purpose of explaining the relevant invention and are not intended to limit the invention. It should also be noted that, for ease of description, only portions relevant to the invention are shown in the accompanying drawings.

[0054] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0055] As mentioned in the background technology, in response to the problems in the existing technology, this application proposes a brake taillight control method, which is suitable for analyzing road conditions and making predictions to rationally control the taillights, providing intuitive and reliable visual warnings to following vehicles in advance. The brake taillight control methods provided in the various embodiments of this application can be executed by electronic equipment.

[0056] Figure 1This is a flow chart of a brake taillight control method provided by an embodiment of the present application. Figure 1 , the brake tail light control method specifically includes:

[0057] S110. For each preset calculation path, determine the deceleration ratio, deceleration increment ratio, and road surface low adhesion ratio corresponding to the preset calculation path based on vehicle sensor information and a preset braking factor.

[0058] Among them, the preset calculation path includes a mathematical model path and a neural network model path. The mathematical model path is a path that uses traditional mathematical calculation methods to process data, and the neural network model path is a path that pre-builds and trains a neural network model for data analysis and processing. Vehicle sensor information is information collected by various sensors on the vehicle, and may include vehicle motion information, such as wheel speed, slip rate, brake pressure, etc. The preset braking factor is experimentally calibrated data used to correct the braking system, tire condition, etc. The deceleration ratio is the ratio of the current deceleration to the maximum available deceleration. The deceleration increment ratio is the ratio of the current deceleration increment to the maximum available deceleration. The road surface low adhesion ratio is a value used to describe whether the road surface is dry or wet. It can be understood that the deceleration ratio, deceleration increment ratio and road surface low adhesion ratio are all between 0 and 1.

[0059] Specifically, each preset calculation path independently calculates the remaining braking space for a single path. When using a mathematical model for path calculation, vehicle sensor information is used through traditional mathematical methods to calculate the deceleration percentage, deceleration increment percentage, and road surface low-adhesion ratio. When using a neural network model for path calculation, vehicle sensor information is input into a pre-established neural network model, which outputs the deceleration percentage, deceleration increment percentage, and road surface low-adhesion ratio.

[0060] It is understandable that the mathematical model path calculation is simple and efficient, but it is difficult to handle complex road conditions. The neural network model path can handle complex road conditions and has higher sensitivity, but has poor interpretability.

[0061] S120: Determine a single-path remaining braking space corresponding to a preset calculation path according to the deceleration ratio, the deceleration increment ratio, and the road surface low adhesion ratio.

[0062] The single-path remaining braking space is the safe braking time of the following vehicle calculated for each preset calculation path.

[0063] Specifically, for each preset calculation path, the deceleration ratio, deceleration increment ratio, road low adhesion ratio and the corresponding preset weights are used for weighted summation to obtain the used braking space. The used braking space is subtracted from the total braking space 1, and the difference is processed to between 0 and 1. The remaining braking space of a single path corresponding to the preset calculation path can be obtained.

[0064] Based on the above example, the remaining braking space for a single path corresponding to the preset calculation path can be determined according to the deceleration ratio, deceleration increment ratio, and road surface low adhesion ratio in the following manner:

[0065] The deceleration ratio, the deceleration increment ratio, and the road surface low adhesion ratio are weighted according to the first coefficient, the second coefficient, and the third coefficient corresponding to the preset calculation path to determine the weighted remaining braking space;

[0066] 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;

[0067] 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 used as the single path remaining braking space corresponding to the preset calculation path;

[0068] If the weighted remaining braking space is less than 0, it is determined that the single path remaining braking space corresponding to the preset calculation path is 0.

[0069] 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 using a weighted calculation method and may not be between 0 and 1.

[0070] Specifically, for each preset calculation path, the first coefficient, second coefficient, and third coefficient corresponding to the preset calculation path are retrieved. The first coefficient is used to weight the deceleration ratio corresponding to the preset calculation path, the second coefficient is used to weight the deceleration increment ratio corresponding to the preset calculation path, and the third coefficient is used to weight the road surface low adhesion ratio corresponding to the preset calculation path. The sum of the three coefficients is used as the used braking space, and the used braking space is subtracted from 1 to obtain the weighted remaining braking space. The weighted remaining braking space needs to be adjusted to between 0 and 1. Therefore, 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 used as the single-path remaining braking space corresponding to the preset calculation path. 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.

[0071] For example, the first, second, and third coefficients reflect the sensitivity to the deceleration ratio, deceleration increment ratio, and low-adhesion road surface ratio, respectively. In the mathematical model path, the first coefficient is the weight corresponding to the deceleration ratio. Its calibration strategy is to collect data from short, sudden braking intervals at the test site to observe the expected warning needs of following vehicles when deceleration is high. If you want following vehicles to be more alert to "currently decelerating significantly," increase the first coefficient. At the same time, taking human factors into consideration, if the first coefficient is too high, a slight application of the brakes could significantly reduce the remaining braking space on a single path and cause noticeable changes in the taillights. Therefore, a value that balances sensitivity and stability is generally selected, for example, within an empirical range of 1.0 to 3.0 (this value can vary depending on the dimension and vehicle braking characteristics). The second coefficient is the weight corresponding to the deceleration increment. Its calibration strategy involves designing a specific test scenario: initially applying the brakes lightly (low deceleration), followed by a rapid, deeper application (sharp deceleration). Through feedback from the following driver or simulation, the threshold at which a stronger warning is needed based on the deceleration increment is determined. For example, a range of experience is 0.5 to 2.0. To suppress minor oscillations, a first-order filter or hysteresis is often used. The third coefficient is the weight corresponding to the low adhesion ratio of the road surface. Its calibration strategy involves braking on simulated wet or snowy surfaces at a test site to observe the safety of following vehicles. If road conditions change rapidly (for example, immediately after entering a flooded section), a slightly higher third coefficient can be set to amplify the risk of low adhesion. For example, a range of experience is 0.2 to 1.0. Avoid setting the third coefficient too high, which could result in a persistent high warning status under normal wet conditions. The mathematical model's path calculation is simple and efficient, and its parameters are intuitive and interpretable, making it easy to implement and troubleshoot. In the neural network model, the corresponding first, second, and third coefficients can be dynamically generated using a reinforcement learning algorithm. The neural network model path can adaptively adjust weights, optimize the remaining braking space assessment according to different scenarios, and handle complex edge cases more robustly.

[0072] Exemplarily, the single-path remaining braking space corresponding to each preset calculation path is determined by the following formula:

[0073] R trad (t) = clamp(1-λ1*r 1trad (t)-λ2*r 2trad (t)-λ3*r 3trad (t), 0, 1)

[0074] R AI (t) = clamp(1-w1(t)*r 1AI (t)-w2(t)*r 2AI (t)-w3(t)*r 3AI (t), 0, 1)

[0075] Among them, R trad (t) is the single path remaining braking space corresponding to the mathematical model path, R AI (t) is the single path remaining braking space corresponding to the neural network model path, λ1, λ2 and λ3 are the first coefficient, second coefficient and third coefficient corresponding to the mathematical model path, w1, w2 and w3 are the first coefficient, second coefficient and third coefficient corresponding to the neural network model path, r 1trad (t) is the deceleration ratio corresponding to the mathematical model path, r 2trad (t) is the deceleration increment ratio corresponding to the mathematical model path, r 3trad (t) is the road surface low adhesion ratio corresponding to the mathematical model path, r 1AI (t) is the deceleration ratio corresponding to the neural network model path, r 2AI (t) is the deceleration increment ratio corresponding to the neural network model path, r 3AI (t) is the road surface low adhesion ratio corresponding to the neural network model path, and clamp(x,0,1) means limiting x to the interval [0,1]. If x<0, then clamp(x,0,1)=0, and if x>1, then clamp(x,0,1)=1.

[0076] S130. Determine a fusion coefficient based on the path reliability corresponding to each preset calculation path and the complexity of the current working condition, and determine a fusion residual braking space based on the fusion coefficient and the single-path residual braking space corresponding to each preset calculation path.

[0077] Path reliability refers to the reliability of the preset calculated path itself when in use, and can be understood as the confidence level during use. Current operating condition complexity describes the operating condition based on the current vehicle driving environment and vehicle motion. The fusion coefficient is the coefficient used to combine the single-path remaining braking space corresponding to the two preset calculated paths. The fused remaining braking space is the value obtained by combining the single-path remaining braking space corresponding to the two preset calculated paths, and is the basis for subsequent control of the vehicle's taillights.

[0078] Specifically, the path reliability corresponding to each preset calculation path is obtained, along with the current operating condition complexity. The first fusion component is determined by combining the path reliability corresponding to each preset calculation path. The second fusion component is determined by analyzing the current operating condition complexity. The fusion coefficient is adaptively determined by combining the first and second fusion components. The fusion coefficient is used to fuse the single-path residual braking space corresponding to each preset calculation path to obtain the fused residual braking space.

[0079] Based on the above example, before determining the fusion coefficient based on the path reliability corresponding to each preset calculation path and the current working condition complexity, the current working condition complexity can be further calculated to determine the fusion coefficient. Specifically, it can be:

[0080] Determine the road adhesion coefficient change rate based on the road adhesion coefficient at the current moment and the road adhesion coefficient at the previous moment corresponding to the mathematical model path;

[0081] Determine the environmental complexity based on various environmental factors;

[0082] The complexity of the current working condition is determined based on the deceleration ratio, road adhesion coefficient change rate and environmental complexity corresponding to the mathematical model path.

[0083] The road adhesion coefficient is used to describe whether the road surface is wet or dry. The road adhesion coefficient change rate describes the speed at which the road adhesion coefficient changes, that is, the speed at which road conditions change. Environmental factors can include weather, brightness, temperature, and other factors. Environmental complexity describes the complexity of the vehicle's surroundings.

[0084] Specifically, the road adhesion coefficient at the current moment and the previous moment corresponding to the mathematical model path are obtained. The difference between the two is then divided by the time interval to calculate the road adhesion coefficient change rate. By comprehensively analyzing various environmental factors, the environmental complexity can be determined. The deceleration percentage, road adhesion coefficient change rate, and environmental complexity corresponding to the mathematical model path are compared, and the maximum value is used as the current operating condition complexity.

[0085] For example, the complexity of the current working condition can be determined in the following manner:

[0086] Ccomp(t) = max(

[0087] k1·normalize(|dμ trad (t) / dt|),

[0088] k2·normalize(|a(t)| / a availtrad (t)),

[0089] k3·normalize(envComplexity) )

[0091] Where Ccomp(t) is the complexity of the current working condition, k1, k2 and k3 are the change rate of the road adhesion coefficient, the deceleration ratio corresponding to the mathematical model path and the weight coefficient corresponding to the environmental complexity, respectively. trad (t) / dt| is the rate of change of the road adhesion coefficient corresponding to the mathematical model path, |a(t)| / aavailtrad (t) is the deceleration ratio corresponding to the mathematical model path, envComplexity is the environment complexity, max() is to find the maximum value, and normalize() is to normalize to the [0,1] interval.

[0092] For example, the environment complexity can be calculated as follows:

[0093] envComplexity=env1·isRaining+env2·isSnowing+env3·(1-normalizedTemp)+env4·isNight

[0094] The same representations as in the above example are not repeated here. isRaining is a binary representation of whether it is raining, isSnowing is a binary representation of whether it is snowing, and isNight is a binary representation of whether it is nighttime. It is understood that the binary representation is 1 if it is raining and 0 if it is not. normalizedTemp is the normalized value of the current temperature. env1, env2, env3, and env4 are the importance coefficients corresponding to the various indicators, such as env1 = 0.5, env2 = 0.7, env3 = 0.3, and env4 = 0.4. The specific values can be adjusted according to actual needs and are not specifically limited here.

[0095] Based on the above example, before determining the fusion coefficient based on the path reliability corresponding to each preset calculation path and the complexity of the current working condition, the path reliability corresponding to the mathematical model path and the path reliability corresponding to the neural network model path can be further determined. Specifically, it can be:

[0096] Obtain the vehicle's electronic control unit health and sensor data quality assessment values;

[0097] Determine the path reliability corresponding to the mathematical model path based on the health of the electronic control unit and the sensor data quality assessment value;

[0098] Obtain the prediction difference between the target model in the neural network model path and the preset evaluation model, as well as the data quality of the vehicle sensor information;

[0099] Determine the model confidence level corresponding to the neural network model path based on the predicted difference and data quality;

[0100] The path reliability corresponding to the neural network model path is determined based on the health of the electronic control unit, the sensor data quality assessment value, and the model confidence.

[0101] Among them, the electronic control unit health is an indicator used to measure the hardware status of the vehicle, with a numerical range of [0,1]. The sensor data quality assessment value is an indicator used to measure the quality of sensor data, which is related to the signal-to-noise ratio and data stability, with a numerical range of [0,1]. The target model is the neural network model used in the neural network model path. The preset evaluation model is a model used to compare the output results with the target model and has a different model structure from the target model. The result prediction difference is the difference between the output results of the target model based on vehicle sensor information and the output results of the preset evaluation model based on vehicle sensor information. Data quality is the adaptability between vehicle sensor information and the training data used to train the target model, and is used to describe the reliability of vehicle sensor information.

[0102] Specifically, the vehicle's electronic control unit health and sensor data quality assessment values are obtained. Combining the electronic control unit health and sensor data quality assessment values, the path reliability corresponding to the mathematical model path can be calculated. Vehicle sensor information is input into the target model in the neural network model path to obtain a first result, which is then input into a preset assessment model to obtain a second result. The difference between the two is used as the result prediction difference. The vehicle sensor information can then be evaluated using a data quality assessment model to determine data quality. The data quality assessment model is pre-trained using valid and invalid input data. A comprehensive analysis of the result prediction difference and data quality can be used to determine the model confidence corresponding to the neural network model path. For example, the normalized result prediction difference and data quality are normalized, and the maximum value is calculated by subtracting the normalized result prediction difference from 1 and the normalized data quality to obtain the model confidence corresponding to the neural network model path. The comprehensive analysis of the electronic control unit health, sensor data quality assessment value, and model confidence can be used to calculate the path reliability corresponding to the neural network model path.

[0103] Exemplarily, the path reliability corresponding to the mathematical model path and the path reliability corresponding to the neural network model path can be calculated in the following manner:

[0104] Crel trad (t)=w hwtrad *hwStatus+w senstrad *sensorQuality

[0105] Crel AI (t)=w hwAI *hwStatus+w sensAI *sensorQuality+w confAI *AI confidence

[0106] Among them, Crel trad(t) is the path reliability corresponding to the mathematical model path, Crel AI (t) is the path reliability corresponding to the neural network model path, hwStatus is the health of the electronic control unit, sensorQuality is the sensor data quality assessment value, AIconfidence is the model confidence corresponding to the neural network model path, w hwtrad and w senstrad is the weight coefficient corresponding to the mathematical model path, w hwAI 、w sensAI and w confAI is the weight coefficient corresponding to the neural network model path, and the sum of the weight coefficients corresponding to the calculation path is uniformly preset to be 1.

[0107] S140: Control the display brightness and display area of the vehicle taillights according to the integrated remaining braking space.

[0108] The display brightness refers to the brightness of the vehicle's taillights, and the display area refers to the illuminated area of the vehicle's taillights.

[0109] Specifically, the fused remaining braking space is converted into a percentage, and the display brightness and display area corresponding to the percentage are determined, and the vehicle taillights are controlled to light up according to the determined display brightness and display area. It can be understood that the signal for controlling the vehicle taillights can be a PWM (Pulse Width Modulation) or a CAN (Controller Area Network) signal.

[0110] Based on the above example, the display brightness and display area of the vehicle taillights can be controlled by integrating the remaining braking space in the following way:

[0111] If the fused remaining braking space is greater than or equal to a first threshold, determining that the display brightness of the vehicle taillights is a preset minimum brightness, and determining that the display area of the vehicle taillights is a preset minimum area;

[0112] If the fused remaining braking space is less than the first threshold and greater than or equal to the second threshold, determining a target ratio based on the fused remaining braking space, the first threshold, and the second threshold, and determining the display brightness of the vehicle taillights based on the target ratio, a preset minimum brightness, and a preset maximum brightness, and determining the display area of the vehicle taillights based on the target ratio, a preset minimum area, and a preset maximum area;

[0113] If the fused remaining braking space is less than the second threshold, the display brightness of the vehicle taillights is determined to be a preset maximum brightness, and the display area of the vehicle taillights is determined to be a preset maximum area.

[0114] The first and second thresholds are pre-set percentage values used to classify different levels. The first threshold is greater than the second threshold, such as 70% for the first threshold and 30% for the second threshold. Specific values can be calibrated based on actual conditions. The preset maximum brightness and preset minimum brightness are the maximum and minimum brightness that can be displayed by vehicle taillights as required by laws and regulations. The preset maximum area and preset minimum area are the maximum and minimum areas that can be displayed by vehicle taillights as required by laws and regulations. The target ratio is used to describe the position of the fused braking space between the first and second thresholds, such as the ratio between the difference between the fused braking space and the second threshold and the difference between the first and second thresholds.

[0115] Specifically, if the fused remaining braking space is greater than or equal to a first threshold, indicating that the remaining safe braking time for the following vehicle is relatively long, the vehicle's taillight display brightness can be determined to be a preset minimum brightness, and the vehicle's taillight display area can be determined to be a preset minimum area. If the fused remaining braking space is less than the first threshold and greater than or equal to a second threshold, indicating that the safe braking time reserved for the following vehicle is limited but not particularly urgent, the display brightness and display area can be dynamically adjusted based on the fused remaining braking space. The ratio of the difference between the fused braking space and the second threshold and the difference between the first threshold and the second threshold is used as a target ratio. According to the target ratio, the vehicle's taillight display brightness is linearly determined between the preset minimum brightness and the preset maximum brightness. Moreover, according to the target ratio, the vehicle's taillight display area is linearly determined between the preset minimum area and the preset maximum area. If the fused remaining braking space is less than the second threshold, indicating emergency braking, the vehicle's taillight display brightness is determined to be a preset maximum brightness, and the vehicle's taillight display area is determined to be a preset maximum area.

[0116] The brake taillight control method provided in an embodiment of the present application determines, for each preset calculation path, the deceleration percentage, deceleration increment percentage, and road surface low-adhesion ratio corresponding to the preset calculation path based on vehicle sensor information and a preset braking factor, to facilitate determining factors to be considered in subsequent taillight control. The method also determines the single-path remaining braking space corresponding to the preset calculation path based on the deceleration percentage, deceleration increment percentage, and road surface low-adhesion ratio, facilitating separate analysis using different preset calculation paths. A fusion coefficient is determined based on the path reliability corresponding to each preset calculation path and the complexity of the current operating conditions, so that the fusion coefficient is adapted to the complexity of each preset calculation path and the current scenario. Furthermore, a fused remaining braking space is determined based on the fusion coefficient and the single-path remaining braking space corresponding to each preset calculation path. This dual-path design adapts to different road and weather conditions. The display brightness and display area of the vehicle taillights are controlled based on the fused remaining braking space, achieving dynamic control of the taillights, adapting to complex and changing road conditions, and providing more intuitive and accurate braking information to following vehicles, thereby reducing the risk of rear-end collisions.

[0117] Figure 2 This is a flow chart of another brake taillight control method provided by an embodiment of the present application. Based on the above embodiments, the calculation process of the deceleration ratio, deceleration increment ratio and road surface low adhesion ratio, the analysis process of the fusion coefficient and the display control process of the vehicle taillights are exemplarily described. Figure 2 , the brake tail light control method specifically includes:

[0118] S210. For each preset calculation path, determine a road adhesion coefficient based on the preset calculation path and according to vehicle sensor information, and determine a road low adhesion ratio corresponding to the preset calculation path based on the road adhesion coefficient.

[0119] Specifically, the road adhesion coefficient μ can be estimated online by analyzing the wheel slip rate and other information in the vehicle sensor information based on the mathematical model path. trad (t), subtract the road adhesion coefficient from 1 to get the road low adhesion ratio. Input the vehicle sensor information into the neural network model in the neural network model path, and output the road adhesion coefficient μ AI (t), subtract the road adhesion coefficient from 1 to obtain the road low adhesion ratio.

[0120] For example, the neural network model in the neural network model path can be expressed as μAI(t) = G(S(t),θ), where the network input is a multidimensional feature vector S(t), which is vehicle sensor information such as wheel speed, slip rate, brake pressure, etc., and θ is the vehicle steering angle. The network structure includes an input layer, a first hidden layer, a second hidden layer, and an output layer. The input layer receives S(t) and θ to obtain 8-12 features. The first hidden layer is a fully connected layer and a ReLU composed of 16-32 units. The second hidden layer is a fully connected layer and a ReLU composed of 8-16 units. The output layer can be a Sigmoid function or a normalized function to make μ AI (t)∈[0,1]. It is understandable that the neural network model can be trained offline using multiple road condition data and fine-tuned online. The neural network model can handle complex road conditions, capture nonlinear relationships that are difficult to identify using mathematical model paths, and is more sensitive to abnormal road conditions (such as partial icing).

[0121] S220: Determine the maximum available deceleration corresponding to the preset calculation path based on the road adhesion coefficient, the preset braking factor, and the acceleration of gravity; and determine the deceleration change rate based on the actual deceleration at the current moment and the actual deceleration at the previous moment.

[0122] The preset braking factor is a calibrated value that takes into account the condition of the brake system and tires. Ideally, it is 1, but can be less than 1 in the event of tire wear, excessive temperature, or brake system degradation. The actual deceleration is the deceleration value measured by the sensor, which may be low-pass filtered to suppress noise. The maximum available deceleration is the maximum deceleration that can be achieved when the vehicle is moving forward, and is a positive number.

[0123] Specifically, for each preset calculation path, the product of the road adhesion coefficient, the preset braking factor, and the acceleration of gravity corresponding to that preset calculation path is used as the maximum available deceleration for that preset calculation path. Furthermore, the ratio of the difference between the actual deceleration at the current moment and the actual deceleration at the previous moment and the time interval is used as the deceleration change rate.

[0124] Exemplarily, the maximum available deceleration corresponding to each preset calculation path is calculated using the following formula:

[0125] a availtrad (t)=μ trad (t)*g*k

[0126] a availAI (t)=μ AI (t)*g*k

[0127] Among them, a availtrad (t) is the maximum available deceleration corresponding to the mathematical model path, μtrad (t) is the road adhesion 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 adhesion coefficient corresponding to the neural network model path, g is the acceleration of gravity, and k is the preset braking factor.

[0128] The deceleration rate of change is calculated using the following formula. It can be understood that the deceleration rate of change corresponding to the two preset calculation paths is the same:

[0129] da(t) / dt=(a(t)-a(t-1)) / T s

[0130] Where da(t) / dt is the rate of change of deceleration, a(t) is the actual deceleration at the current moment, a(t-1) is the actual deceleration at the previous moment, and T s is the time interval.

[0131] S230. Determine the deceleration ratio corresponding to the preset calculation path based on the actual deceleration at the current moment and the maximum available deceleration corresponding to the preset calculation path; determine the deceleration increment ratio corresponding to the preset calculation path based on the deceleration change rate and the maximum available deceleration corresponding to the preset calculation path.

[0132] Specifically, for each preset calculation path, the absolute value of the actual deceleration at the current moment is divided by the maximum available deceleration corresponding to the preset calculation path to obtain the deceleration ratio corresponding to the preset calculation path, and the deceleration change rate is divided by the maximum available deceleration corresponding to the preset calculation path to obtain the deceleration increment ratio corresponding to the preset calculation path.

[0133] Exemplarily, the deceleration ratio and deceleration increment ratio corresponding to each preset calculation path are calculated using the following formula:

[0134] r 1trad (t) = |a(t)| / a availtrad (t)

[0135] r 2trad (t) = |da(t) / dt| / a availtrad (t)

[0136] r 1AI (t) = |a(t)| / a availAI (t)

[0137] r 2AI (t) = |da(t) / dt| / a availAI (t)

[0138] The same expressions as above are not repeated here. 1trad (t) is the deceleration ratio corresponding to the mathematical model path, r 2trad (t) is the deceleration increment ratio corresponding to the mathematical model path, r 1AI (t) is the deceleration ratio corresponding to the neural network model path, r 2AI (t) is the deceleration increment ratio corresponding to the neural network model path.

[0139] S240: Determine the single-path remaining braking space corresponding to the preset calculation path according to the deceleration ratio, the deceleration increment ratio, and the road surface low adhesion ratio.

[0140] S250. Determine the reliability difference based on the path reliability corresponding to the mathematical model path and the path reliability of the neural network model path, and determine the first addend based on the reliability difference and the preset reliability difference sensitivity; determine the second addend based on the current working condition complexity, the preset complexity and the preset complexity sensitivity.

[0141] Among them, the reliability difference is the difference between the path reliability corresponding to the mathematical model path and the neural network model path. The preset complexity is a pre-set complexity value used to judge the degree of difficulty, which can usually be set to 0.5, and can also be adjusted according to needs. The first addend and the second addend are intermediate values in the calculation process. The preset reliability difference sensitivity and the preset complexity sensitivity are pre-set values used to describe the importance of the reliability difference and the complexity of the current working condition. For example, the reliability difference sensitivity is 5.0, the preset complexity sensitivity is 3.0, etc.

[0142] Specifically, the difference between the path reliability corresponding to the mathematical model path and the path reliability of the neural network model path is calculated as the reliability difference, and the product of the reliability difference and a preset reliability difference sensitivity is determined as the first addend. The product of the difference between the preset complexity and the current operating condition complexity and the preset complexity sensitivity is determined as the second addend.

[0143] S260: Process the sum of the first addend and the second addend using a preset regression function to obtain a fusion coefficient.

[0144] The preset regression function is a pre-set regression function, which may be a sigmoid function or the like.

[0145] Specifically, the first addend and the second addend are summed, and the obtained sum is substituted into a preset regression function to obtain a fusion coefficient.

[0146] For example, the fusion coefficient can be calculated by the following formula:

[0147] α(t) = sigmoid(τ1*( Crel trad (t) - Crel AI (t)) + τ2*(C - Ccomp(t)))

[0148] Among them, α(t) is the fusion coefficient, Crel trad (t) is the path reliability corresponding to the mathematical model path, Crel AI (t) is the path reliability corresponding to the neural network model path, Ccomp(t) is the complexity of the current working condition, C is the preset complexity, τ1 is the preset reliability difference sensitivity, τ2 is the preset complexity sensitivity, and sigmoid(x) = 1 / (1+e^(-x)), that is, the result is mapped to the interval [0,1].

[0149] 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 scenario is simple, α(t) approaches 1, indicating a bias towards the mathematical model path; when the neural network model path is more reliable and the scenario is complex, α(t) approaches 0, indicating a bias towards the neural network model path; when the reliability of the two preset calculation paths is comparable and the complexity is medium, α(t) approaches 0.5, indicating that they can be balanced and integrated.

[0150] S270 : Determine the fusion remaining braking space according to the fusion coefficient and the single-path remaining braking space corresponding to each preset calculation path.

[0151] Exemplarily, the fusion remaining braking space may be determined in the following manner:

[0152] R final (t) = α(t)·R trad (t) + (1-α(t))·R AI (t)

[0153] Among them, R final (t) is the fusion braking space, R trad (t) is the single path remaining braking space corresponding to the mathematical model path, R AI (t) is the single path remaining braking space corresponding to the neural network model path, and α(t) is the fusion coefficient.

[0154] S280 , determining the target remaining braking space at the current moment according to the preset filter coefficient, the fused remaining braking space, and the target remaining braking space at the previous moment; and updating the target remaining braking space at the current moment based on a hysteresis mechanism.

[0155] The preset filter coefficient is a pre-set coefficient for filtering, and the target remaining braking space is the remaining braking space after filtering and hysteresis processing are performed on the fused remaining braking space.

[0156] Specifically, based on the preset filter coefficient and the target remaining braking space at the previous moment, the fused remaining braking space is processed to obtain the target remaining braking space at the current moment, and the processed target remaining braking space at the current moment is processed using a hysteresis mechanism to update the target remaining braking space at the current moment. A hysteresis mechanism is introduced to prevent oscillation near the threshold.

[0157] Exemplarily, the fused residual braking space is filtered based on the following formula:

[0158] Rfiltered final (t) = β·Rfiltered final (t-1) + (1-β)·R final (t)

[0159] Among them, β is the preset filter coefficient, usually ranging from 0.3 to 0.7, Rfiltered final (t) is the target remaining braking space at the current moment, Rfiltered final (t-1) is the target remaining braking space at the previous moment, R final To integrate the remaining braking space.

[0160] S290: Control the display brightness and display area of the vehicle taillights according to the updated target remaining braking space at the current moment.

[0161] Specifically, similar to S140 , the updated target remaining braking space at the current moment may be used instead of the fused remaining braking space to perform display control on the vehicle taillights.

[0162] The brake taillight control method provided by the embodiment of the present application determines the road adhesion coefficient for each preset calculation path based on the preset calculation path and according to vehicle sensor information, and determines the road low adhesion ratio corresponding to the preset calculation path according to the road adhesion coefficient, and determines the maximum available deceleration corresponding to the preset calculation path according to the road adhesion coefficient, the preset braking factor and the acceleration of gravity; determines the deceleration change rate according to the actual deceleration at the current moment and the actual deceleration at the previous moment, and determines the deceleration proportion 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; determines the deceleration increment proportion corresponding to the preset calculation path according to the deceleration change rate and the maximum available deceleration corresponding to the preset calculation path, so as to more accurately calculate the basic information required for each preset calculation path, and then determines the reliability difference according to the path reliability corresponding to the mathematical model path and the path reliability of the neural network model path, and determines the reliability difference according to the reliability difference and the preset reliability difference Different sensitivity, determine the first addend, determine the second addend according to the complexity of the current working condition, the preset complexity and the sensitivity of the preset complexity, and process the sum of the first addend and the second addend through the preset regression function to obtain the fusion coefficient, so as to flexibly determine the fusion coefficient by fully considering the reliability of each preset calculation path and the complexity of the current working condition, and determine the target remaining braking space at the current moment according to the preset filter coefficient, the fusion residual braking space and the target remaining braking space at the previous moment; based on the hysteresis mechanism, update the target remaining braking space at the current moment to improve the stability and effectiveness of the target remaining braking space, realize high-precision real-time evaluation of the remaining braking space and dynamic control of the taillights, can adapt to complex and changeable road conditions, have the ability to predict changes in the road adhesion coefficient, and provide intuitive and reliable visual warnings to the following vehicles in advance, and the adaptive fusion mechanism ensures the balance between stability and sensitivity in various scenarios. By optimizing the taillight display strategy, it effectively reduces the risk of rear-end collisions and improves road driving safety.

[0163] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 3 As shown, the electronic device 300 includes one or more processors 301 and a memory 302 .

[0164] The processor 301 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 300 to perform desired functions.

[0165] Memory 302 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Non-volatile memory may include, for example, read-only memory (ROM), a hard disk, or flash memory. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 301 may execute the program instructions to implement the brake taillight control method described above in any embodiment of the present application and / or other desired functions. The computer-readable storage medium may also store various contents, such as initial external parameters and threshold values.

[0166] In one example, electronic device 300 may further include an input device 303 and an output device 304, which are interconnected via a bus system and / or other connection mechanisms (not shown). Input device 303 may include, for example, a keyboard, a mouse, etc. Output device 304 may output various information to the outside, including warning information, braking force, etc. Output device 304 may include, for example, a display, a speaker, a printer, a communication network, and remote output devices connected thereto.

[0167] Of course, to simplify, Figure 3 Only some of the components related to the present application in the electronic device 300 are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, the electronic device 300 may further include any other appropriate components according to specific application scenarios.

[0168] In addition to the above methods and devices, embodiments of the present application may also be a computer program product, which includes computer program instructions. When the computer program instructions are executed by a processor, the processor executes the steps of the brake tail light control method provided by any embodiment of the present application.

[0169] The computer program product may be written in any combination of one or more programming languages to implement the program code for performing the operations of the embodiments of the present application, including object-oriented programming languages such as Java, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0170] In addition, an embodiment of the present application may also be a computer-readable storage medium having computer program instructions stored thereon. When the computer program instructions are executed by a processor, the processor executes the steps of the brake taillight control method provided by any embodiment of the present application.

[0171] The computer-readable storage medium may be 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 electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection with 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0172] It should be noted that the terms used in this application are only for describing specific embodiments and are not intended to limit the scope of this application. As shown in the specification and claims of this application, unless the context clearly indicates an exception, the words "one", "an", "a kind of" and / or "the" do not specifically refer to the singular and may also include the plural. The terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method or device. In the absence of further restrictions, the elements defined by the sentence "comprise a..." do not exclude the presence of other identical elements in the process, method or device comprising the elements.

[0173] It should also be noted that the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on this application. Unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", etc. should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or an indirect connection through an intermediate medium, or it can be a communication between the internal parts of two elements. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0174] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. The above is only the preferred implementation method of this application. It should be pointed out that due to the limitations of textual expression, there are objectively infinite specific structures. For ordinary technicians in this technical field, without departing from the principles of the present invention, they can also make several improvements, modifications or changes, and can also combine the above technical features in an appropriate manner; these improvements, modifications, changes or combinations, or the direct application of the inventive concept and technical solution to other occasions without improvement, should be regarded as the scope of protection of this application.

Claims

1. A brake tail light control method, characterized in that: include: For each preset calculation path, the deceleration percentage, deceleration increment percentage, and road surface low-adhesion ratio corresponding to the preset calculation path are determined based on vehicle sensor information and a preset braking factor. The deceleration percentage is the ratio of the actual deceleration to the corresponding maximum available deceleration, the deceleration increment percentage is the ratio of the deceleration change rate to the maximum available deceleration, and the road surface low-adhesion ratio is 1 minus the road surface adhesion coefficient. determining a single-path remaining braking space corresponding to the preset calculation path according to the deceleration ratio, the deceleration increment ratio, and the road surface low adhesion ratio; Determine a fusion coefficient based on the path reliability corresponding to each preset calculation path and the complexity of the current working condition, and determine a fusion residual braking space based on the fusion coefficient and the single-path residual braking space corresponding to each preset calculation path; Controlling the display brightness and display area of the vehicle taillights according to the fused remaining braking space; The preset calculation path includes a mathematical model path and a neural network model path; the complexity of the current operating condition is determined based on the deceleration ratio, road adhesion coefficient change rate, and environmental complexity corresponding to the mathematical model path; and the path reliability corresponding to the mathematical model path is determined based on the health of the vehicle's electronic control unit and the sensor data quality assessment value; The path reliability corresponding to the neural network model path is determined according to the health of the electronic control unit, the sensor data quality assessment value and the corresponding model confidence.

2. The method according to claim 1, characterized in that The determining, based on the vehicle sensor information and the preset braking factor, the deceleration ratio, the deceleration increment ratio, and the road surface low adhesion ratio corresponding to the preset calculation path includes: Based on the preset calculation path, determining a road adhesion coefficient according to vehicle sensor information, and determining a road low adhesion ratio corresponding to the preset calculation path according to the road adhesion coefficient; determining a maximum available deceleration corresponding to the preset calculation path according to the road adhesion coefficient, a preset braking factor, and gravity acceleration; Determine the deceleration change rate based on 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; The deceleration increment ratio corresponding to the preset calculation path is determined according to the deceleration change rate and the maximum available deceleration corresponding to the preset calculation path.

3. The method according to claim 1, characterized in that The determining, according to the deceleration ratio, the deceleration increment ratio, and the road surface low adhesion ratio, of the single-path remaining braking space corresponding to the preset calculation path includes: 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 the preset calculation path, respectively, to determine a weighted remaining 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, the weighted remaining braking space is used as the single-path remaining braking space corresponding to the preset calculation path; If the weighted remaining braking space is less than 0, it is determined that the single-path remaining braking space corresponding to the preset calculation path is 0.

4. The method according to claim 1, wherein The step of determining the fusion coefficient based on the path reliability corresponding to each preset calculation path and the complexity of the current working condition includes: Determining a reliability difference 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 and a preset reliability difference sensitivity; Determining a second addend according to the current operating condition complexity, the preset complexity, and the preset complexity sensitivity; The sum of the first addend and the second addend is processed by a preset regression function to obtain a fusion coefficient.

5. The method according to claim 1, wherein 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 includes: Obtain the vehicle's electronic control unit health and sensor data quality assessment values; determining a path reliability corresponding to the mathematical model path according to the health of the electronic control unit and the sensor data quality assessment value; Obtaining a prediction difference between a target model in the neural network model path and a preset evaluation model and data quality of the vehicle sensor information; Determining the model confidence corresponding to the neural network model path based on the result prediction difference and the data quality; The path reliability corresponding to the neural network model path is determined according to the health of the electronic control unit, the sensor data quality assessment value and the model confidence.

6. The method according to claim 1, characterized in that The controlling of the display brightness and display area of the vehicle taillights according to the integrated remaining braking space includes: If the fused remaining braking space is greater than or equal to a first threshold, determining that the display brightness of the vehicle taillights is a preset minimum brightness, and determining that the display area of the vehicle taillights is a preset minimum area; If the fused remaining braking space is less than the first threshold and greater than or equal to the second threshold, determining a target ratio based on the fused remaining braking space, the first threshold, and the second threshold, determining a display brightness of the vehicle taillights based on the target ratio, the preset minimum brightness, and the preset maximum brightness, and determining a display area of the vehicle taillights based on the target ratio, the preset minimum area, and the preset maximum area; If the fused remaining braking space is less than the second threshold, the display brightness of the vehicle taillights is determined to be the preset maximum brightness, and the display area of the vehicle taillights is determined to be the preset maximum area.

7. The method according to claim 1, characterized in that The controlling of the display brightness and display area of the vehicle taillights according to the integrated remaining braking space includes: determining the target remaining braking space at the current moment according to a preset filter coefficient, the fused remaining braking space, and the target remaining braking space at the previous moment; Based on the hysteresis mechanism, updating the target remaining braking space at the current moment; The display brightness and display area of the vehicle taillights are controlled according to the updated target remaining braking space at the current moment.

8. An electronic device, characterized in that: The electronic device comprises: processor and memory; The processor is configured to execute the steps of the brake tail light control method according to any one of claims 1 to 7 by calling the program or instructions stored in the memory.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a program or instruction, which enables a computer to execute the steps of the brake tail light control method according to any one of claims 1 to 7.

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