Electronic rearview mirror parameter intelligent management method for reducing blind areas

Abstract

The invention relates to the technical field of vehicle engineering, and discloses an electronic rearview mirror parameter intelligent management method for reducing blind areas, according to the electronic rearview mirror parameter intelligent management method for reducing blind areas, the visual angle range of a camera is automatically identified and adjusted according to the real-time vehicle motion state, and the influence of blind area increase during high-speed driving is compensated; dynamically adjusting the sensitivity of an image sensor based on the change of the light intensity of the external environment, and optimizing the display quality under a low-light condition: configuring a picture splicing algorithm parameter according to the personalized view field requirement of a driver, and realizing adaptive optimization of different driving habits; the alarm threshold value is set according to the obstacle approaching speed and distance, the false triggering and missing report rate is effectively reduced, and the driving safety is effectively improved.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle engineering, and in particular to an intelligent management method for electronic rearview mirror parameters for reducing blind spots. Background Art

[0002] In the field of automotive safety, an innovative method for intelligent management of electronic rearview mirror parameters has emerged, greatly improving driving safety and driving experience. This intelligent management method focuses on the optimization of five key aspects and effectively solves many problems existing in traditional rearview mirrors.

[0003] In terms of camera viewing angle range control, this method demonstrates excellent real-time adaptability. When the vehicle accelerates, the system will automatically adjust the camera viewing angle to ensure that blind spots at high speeds can be fully monitored, greatly reducing the safety risks caused by blind spots.

[0004] In terms of coping with changes in light, the image sensor sensitivity control function plays an important role. The system can sensitively perceive the intensity of external light and automatically increase the sensor sensitivity at night or in low-light environments, allowing the driver to always see the rear and surrounding conditions clearly, effectively solving the problem of blurred display of traditional rearview mirrors in poor light.

[0005] Taking into account the personalized needs of different drivers, this method allows users to set their preferred field of view parameters. During driving, the system can also dynamically adjust the picture stitching algorithm to suit personal driving habits and provide a more comfortable and safer field of view experience.

[0006] For early warning of potential obstacles, the system uses intelligent algorithms to adjust the alarm threshold in real time based on the approach speed and distance of the obstacle. This can not only avoid frequent and unnecessary alarms that interfere with driving, but also ensure that the alarm sounds in time at critical moments and no potential dangers are missed.

[0007] Safety monitoring when the vehicle is turning has also been optimized. The system instantly adjusts the size of the side monitoring area according to the vehicle's turning angle and direction, accurately captures potential blind spots in curves, and provides the driver with more comprehensive turning safety protection.

[0008] This intelligent management method of electronic rearview mirror parameters, with its innovative optimization in multiple key areas, brings unprecedented safety and convenience to modern driving and redefines the functional standards of vehicle rearview mirrors.

[0009] In summary, this method uses advanced sensor technology and intelligent algorithms to achieve multi-parameter linkage control, significantly reducing visual blind spots during driving and improving driving safety and intelligence. Summary of the invention

[0010] In order to solve the problems raised by the above background technology, the present application provides an intelligent management method for electronic rearview mirror parameters for reducing blind spots.

[0011] The present application provides an electronic rearview mirror parameter intelligent management method for reducing blind spots, which adopts the following technical solutions: An electronic rearview mirror parameter intelligent management method for reducing blind spots, comprising: S101, automatically identifying and adjusting the camera viewing angle according to the real-time vehicle motion state to compensate for the effect of increased blind spots when driving at high speed; S102, dynamically adjusting the sensitivity of the image sensor based on changes in ambient light intensity to optimize display quality under low light conditions; S103, configuring the image stitching algorithm parameters according to the driver's personalized vision requirements to achieve adaptive optimization for different driving habits; S104. Setting an alarm threshold according to the obstacle approach speed and distance to effectively reduce false triggering and missed alarm rates.

[0012] Preferably, the method for automatically identifying and adjusting the camera viewing angle range according to the real-time vehicle motion state includes: Get the vehicle's current speed V and acceleration A; The dynamic field of view adjustment angle θ is calculated according to the formula: θ=arcsin(k*V+b*A), where k and b are coefficients related to speed and acceleration, respectively, and are used to adapt to the optimal field of view at different speeds; θ is greater than the maximum allowable angle θ_max, and θ is set to θ_max; Adjust the angle of the left or right camera based on the calculated θ value.

[0013] Preferably, the adjusting and controlling the sensitivity of the image sensor based on the change of the external environment light intensity further comprises: Get the ambient light intensity I luminous flux value; Determine the base sensitivity S0 and set it as the default parameter of the sensor; Use the formula to adjust the sensitivity: S=max(S_min,S0+m(I-L_ref)), where L_ref is the reference light intensity, m is the sensitivity gain coefficient, and S_min is the lower limit to ensure the minimum display quality; Increases sensitivity by a preset increment for darker conditions when driving at night.

[0014] Preferably, when the ambient light is too low, an additional near-infrared fill light system is used to enhance the camera effect: Get the ambient light intensity I and judge I <I_thre; Turn on the vehicle-mounted infrared illuminator to increase the effective light brightness; At the same time, increase the image noise filtering level to suppress noise formation; Adjust the fill light output power W_T according to the actual working temperature T: W_T=W_base*exp(α*(T-T_ambient)), where W_base represents the power base under normal temperature conditions, and α is the thermal sensitivity coefficient.

[0015] Preferably, the image stitching algorithm parameters are adjusted according to the driver's personalized vision requirements: Identify the driver and load the preference dataset D through historical records; Select the corresponding data model M to handle different perspective transformations; Calculate the visual comfort function F(x)=1 / (1+β*A)γV^2, where x represents an element in the user setting item set, A represents the angle deviation between the adjacent frames, and V is the instantaneous vehicle speed. β and γ are influencing factors to balance accuracy and fluency; Based on the feedback mechanism, the collage parameter set P is continuously optimized to optimize the user experience; Based on driving behavior prediction, the image area that needs to be displayed in the future: Analyze the manipulation input X in multiple consecutive sample segments in the past; Predict the next key monitoring direction y through machine learning; Establish a custom boundary equation L(x)=wx+b to define the range to be displayed and track it in real time; When the maximum viewing angle ω is exceeded, the proportion of other non-critical parts is compressed to ensure full coverage of safety monitoring. Here, the empirical value of ω is 45°±5%, w depends on the steering wheel rotation arc and b=-65.

[0016] Preferably, after setting the alarm threshold, the corresponding control alarm strategy is executed: Monitor the distance d from the object to the vehicle and the closing rate v_close; Set the basic threshold T_basic and the comprehensive danger level z after weighted average of weights w_dist and w_speed; The decision tree rule is used to determine the final warning level G = G (z) = z / z_high + 7 × sign [(v_rel / d) - τ], z_high represents the critical value, when (v_rel / d) < τ, the speed component is not considered, v_rel represents the relative speed of the two and τ is the theoretical limit, which is used to avoid finding the right position between false alarms and slow reactions; Set graded sound effects and screen color change warning methods according to different scenarios; Add GPS and inertial navigation equipment to read vehicle location information; Calculate the expected collision point c, and update the original evaluation model input x′ according to the vector difference of cl; Compare the instantaneous measurement x′ with the outcomes of similar situations s stored in the database and find the expectation e; e>δ triggers an alarm prompt in advance to improve safety.

[0017] Preferably, the steering axis α of the front tire and the inclination angle β of the vehicle body are detected; Calculate the size of the potential unobserved interval U area S_u caused by yaw; Through the formula S_U=S_default+k*(αβ)*sin((L+w) / d), where L is the basic wheelbase, w is the driving bandwidth, d is the turning radius, and k is the empirical constant factor used to correct the influence of the deformation effect; Immediately expand to the necessary side wide-angle lens field of view to compensate for the loss and ensure that the surrounding environment is fully visible.

[0018] Preferably, the relevant algorithm parameters are automatically adapted based on the steering assistance service provided by the lane keeping assist system LKAS: When LKAS is enabled, the lateral field of view adjustment unit will be activated simultaneously; Collect the direction correction proposal δ_dir given by LKAS; Adjust the overlap ratio between the stitched images ρ = max((δ_dir)^k, 3), where ρ is directly related to the merged image quality and smoothness; Ensure that every small turn can obtain the best presentation result and maintain clear and stable visual output even in complex road conditions.

[0019] Preferably, the use of radar or laser detection of road conditions ahead, including but not limited to the slope i and curvature ρ_curva; Combine i,ρ_curva to calculate the new perspective expansion requirement Δθ_new; A non-fixed formula Δθnew=arcos[√{(1(ρ_car / ρ_max)^2)}*cos(i)] is introduced to determine the dynamic change of viewing angle. The parameters in the formula are interpreted as: the maximum tilt value ρ_max under the comfort limit of the cabin and the combined application of the current road curvature description parameter ρ_car and the longitudinal slope index i.

[0020] In summary, the present application includes at least one of the following beneficial technical effects: The present invention obtains the vehicle speed V and acceleration A, calculates the dynamic field of view adjustment angle θ according to the formula, and then adjusts the camera viewing angle range to compensate for the increase in blind spots when driving at high speed, expands the field of view of the vehicle when driving at high speed, and reduces the risk of accidents caused by blind spots. At the same time, according to the changes in the external ambient light intensity, the sensitivity of the image sensor is dynamically adjusted to obtain the ambient light intensity, and the sensitivity is adjusted using the formula. The preset increment is also increased at night. When the ambient light is too low, a near-infrared fill light system is used, and the fill light output power is adjusted according to the actual working temperature. At the same time, the image noise filtering level is increased to ensure that the image is clear in a low-light environment, providing the driver with reliable visual information.

[0021] By identifying the driver's identity, loading the preference data set, selecting the corresponding data model, calculating the visual comfort function, and optimizing the collage parameter set based on the feedback mechanism, the driver's personalized vision needs can be met. In addition, the future image display area can be predicted based on driving behavior, and the ratio of non-critical parts can be compressed when the maximum viewing angle is exceeded, thereby improving visual comfort and safety during driving.

[0022] Monitor the distance d from the object to the vehicle and the closing rate v_close, set basic thresholds and weights, use decision tree rules to determine the warning level, set graded sound effects and screen color change warning methods according to different scenarios, and combine GPS and inertial navigation equipment to obtain vehicle location information, calculate the expected collision point, compare the database to obtain expectations, trigger the alarm in advance, improve the reliability of the alarm system, and reduce unnecessary interference and potential dangers. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 The invention is a flow chart of an intelligent management method of electronic rearview mirror parameters for reducing blind spots. DETAILED DESCRIPTION

[0024] Embodiments of the present application are described in detail below, examples of which are shown in the accompanying drawings.

[0025] In the description of this specification, the description with reference to the terms "certain embodiments", "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiments or examples are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0026] The present application embodiment discloses an electronic rearview mirror parameter intelligent management method for reducing blind spots, referring to Figure 1,include: S101, automatically identifying and adjusting the camera viewing angle according to the real-time vehicle motion state to compensate for the effect of increased blind spots when driving at high speed; S102, dynamically adjusting the sensitivity of the image sensor based on changes in ambient light intensity to optimize display quality under low light conditions; S103, configuring the image stitching algorithm parameters according to the driver's personalized vision requirements to achieve adaptive optimization for different driving habits; S104. Setting an alarm threshold according to the obstacle approach speed and distance to effectively reduce false triggering and missed alarm rates.

[0027] Methods for automatically identifying and adjusting the camera viewing range according to the real-time vehicle motion state include: Get the vehicle's current speed V and acceleration A; The dynamic field of view adjustment angle θ is calculated according to the formula: θ=arcsin(k*V+b*A), where k and b are coefficients related to speed and acceleration, respectively, and are used to adapt to the optimal field of view at different speeds; θ is greater than the maximum allowable angle θ_max, and θ is set to θ_max; Adjust the angle of the left or right camera based on the calculated θ value.

[0028] The sensitivity of the image sensor is further controlled based on the change of the external ambient light intensity, including: Get the ambient light intensity I luminous flux value; Determine the base sensitivity S0 and set it as the default parameter of the sensor; Use the formula to adjust the sensitivity: S=max(S_min,S0+m(I-L_ref)), where L_ref is the reference light intensity, m is the sensitivity gain coefficient, and S_min is the lower limit to ensure the minimum display quality; Increases sensitivity by a preset increment for darker conditions when driving at night.

[0029] When the ambient light is too low, an additional near-infrared fill light system is used to enhance the camera effect: Get the ambient light intensity I and judge I <I_thre; Turn on the vehicle-mounted infrared illuminator to increase the effective light brightness; At the same time, increase the image noise filtering level to suppress noise formation; Adjust the fill light output power W_T according to the actual working temperature T: W_T=W_base*exp(α*(T-T_ambient)), where W_base represents the power base under normal temperature conditions, and α is the thermal sensitivity coefficient.

[0030] Adjust the image stitching algorithm parameters according to the driver's personalized vision requirements: Identify the driver and load the preference dataset D through historical records; Select the corresponding data model M to handle different perspective transformations; Calculate the visual comfort function F(x)=1 / (1+β*A)γV^2, where x represents an element in the user setting item set, A represents the angle deviation between the front and rear adjacent frames, and V is the instantaneous vehicle speed. β and γ are influencing factors, β balances accuracy and γ affects smoothness; Based on the feedback mechanism, the collage parameter set P is continuously optimized to optimize the user experience; Based on driving behavior prediction, the image area that needs to be displayed in the future: Analyze the manipulation input X in multiple consecutive sample segments in the past; Predict the next key monitoring direction y through machine learning; Establish a custom boundary equation L(x)=wx+b to define the range to be displayed and track it in real time; When the maximum viewing angle ω is exceeded, the proportion of other non-critical parts is compressed to ensure full coverage of safety monitoring. Here, the empirical value of ω is 45°±5%, w depends on the steering wheel rotation arc and b=-65.

[0031] After setting the alarm threshold, execute the corresponding control alarm strategy: Monitor the distance d from the object to the vehicle and the closing rate v_close; Set the basic threshold T_basic and the comprehensive danger level z after weighted average of weights w_dist and w_speed; The decision tree rule is used to determine the final warning level G = G (z) = z / z_high + 7 × sign [(v_rel / d) - τ], z_high represents the critical value, when (v_rel / d) < τ, the speed component is not considered, v_rel represents the relative speed of the two and τ is the theoretical limit, which is used to avoid finding the right position between false alarms and slow reactions; Set graded sound effects and screen color change warning methods according to different scenarios; Add GPS and inertial navigation equipment to read vehicle location information; Calculate the expected collision point c, and update the original evaluation model input x′ according to the vector difference of cl; Compare the instantaneous measurement x′ with the outcomes of similar situations s stored in the database and find the expectation e; e>δ triggers an alarm prompt in advance to improve safety.

[0032] Detecting the front tire steering axis α and the body inclination angle β; Calculate the size of the potential unobserved interval U area S_u caused by yaw; Through the formula S_U=S_default+k*(αβ)*sin((L+w) / d), where L is the basic wheelbase, w is the driving bandwidth, d is the turning radius, and k is the empirical constant factor used to correct the influence of the deformation effect; Immediately expand to the necessary side wide-angle lens field of view to compensate for the loss and ensure that the surrounding environment is fully visible.

[0033] Automatically adapt relevant algorithm parameters based on the steering assistance service provided by the lane keeping assist system LKAS: When LKAS is enabled, the lateral field of view adjustment unit will be activated simultaneously; Collect the direction correction proposal δ_dir given by LKAS; Adjust the overlap ratio between the stitched images ρ = max((δ_dir)^k, 3), where ρ is directly related to the merged image quality and smoothness; Ensure that every small turn can obtain the best presentation result and maintain clear and stable visual output even in complex road conditions.

[0034] Use radar or laser to detect the road conditions ahead, including but not limited to the slope i and curvature ρ_curva; Combine i,ρ_curva to calculate the new perspective expansion requirement Δθ_new; A non-fixed formula Δθnew=arcos[√{(1(ρ_car / ρ_max)^2)}*cos(i)] is introduced to determine the dynamic change of viewing angle. The parameters in the formula are interpreted as: the maximum tilt value ρ_max under the comfort limit of the cabin and the combined application of the current road curvature description parameter ρ_car and the longitudinal slope index i.

[0035] Next, the present invention describes an intelligent management method for electronic rearview mirror parameters for reducing blind spots. The method includes a series of intelligent control steps to ensure that the camera viewing angle range, image sensor sensitivity, image stitching algorithm, alarm threshold setting and side monitoring area size can be effectively optimized under different driving environments.

[0036] In the first step, the system first adjusts the camera's viewing angle according to the vehicle's real-time motion state to solve the problem of increased blind spots when driving at high speeds. The specific implementation method is to obtain vehicle speed information through on-board sensors and transmit it to the central controller. The controller determines whether it is necessary to adjust the angle and focal length of the wide-angle lens or fisheye camera in the rearview mirror based on preset logical rules and combined with vehicle speed data. When the vehicle speed exceeds a certain threshold, such as 80 kilometers per hour in highway driving, the system automatically increases the effective monitoring angle of the camera equipment so that the driver can obtain a wider and unobstructed picture perspective; if it is lower than this limited speed value, the normal working mode is maintained. In one embodiment, if the vehicle is accelerating to a speed of 120 kilometers per hour to overtake, during this process the electronic rearview mirror will dynamically adapt to the rapid movement of the vehicle, expand the visual area to cover more road conditions, and ensure driving safety.

[0037] The second step is to adjust the photosensitivity setting of the image sensor in the camera in time according to the changes in the light intensity of the external environment, so as to cope with the problem of poor display quality under low light conditions. The vehicle is equipped with a light sensor to continuously measure the external light level and transmit the relevant information to the main control unit, which changes the working mode of the CMOS chip according to the predetermined strategy to improve image clarity and color reproduction accuracy. When night falls and the surrounding area is dim due to the lack of street lights and other reasons, the intelligent management system begins to reduce the saturation limit point of each pixel on the sensor and enhances the overall gain amplification function to preserve the image details as much as possible. Specifically, on a dark and cloudy night without moonlight, the electronic rearview mirror will recognize that the current environment is very dim and improve the display effect by increasing the brightness and sharpening the edge contour to help the driver see the situation on both sides of the road.

[0038] During the operation of the vehicle, the vehicle's current speed V (usually in meters per second or kilometers per hour) and acceleration A (usually in meters per second squared) are obtained in real time through the vehicle's own speed sensors (such as wheel speed sensors) and acceleration sensors (such as accelerometers). These data are the basic information for subsequent dynamic field of view adjustment, because the vehicle's speed and acceleration status will affect the driver's or related systems' needs to observe the surrounding environment. "The dynamic field of view adjustment angle is calculated according to the formula \(\theta\): \(\theta=\arcsin(k*V+b*A)\), where k and b are coefficients related to speed and acceleration, respectively, to adapt to the optimal field of view at different speeds." Here, a specific formula is used to calculate the angle \(\theta\) that the camera needs to adjust. The \(k*V+b*A\) part of the formula comprehensively considers the impact of the vehicle's speed V and acceleration A, k and b are pre-set coefficients, and their values ​​have been optimized to determine a suitable field of view angle according to different speeds and acceleration conditions to meet the best needs of the vehicle for observing the surrounding environment under different driving conditions. The \(\arcsin\) function (inverse sine function) is used to convert the calculated value into an angle value to obtain the final dynamic field of view adjustment angle \(\theta\). Due to the limitations of the camera's mechanical structure or other practical factors, there is a maximum allowable angle \(\theta_{max}\).

[0039] When the dynamic field of view adjustment angle \(\theta\) calculated by the formula exceeds the maximum allowable angle, in order to avoid the camera exceeding its operable range or being damaged, \(\theta\) is forcibly set to \(\theta_{max}\) to ensure the normal operation of the system and the safety of the camera. According to the dynamic field of view adjustment angle \(\theta\) calculated and processed (if any) in the previous steps, the camera angle on the left or right side of the vehicle is adjusted accordingly. By adjusting the angle of the camera, it can capture image information in a more appropriate range, provide better field of view assistance for the driver, or provide more accurate environmental data for related systems of the vehicle (such as autonomous driving systems).

[0040] During the operation of LKAS, the system will calculate the proposal \(\delta_{dir}\) for correcting the vehicle's driving direction based on the vehicle's current driving status and lane conditions. This proposal indicates the direction and amplitude that LKAS believes the vehicle should adjust in order to stay within the lane. The system will collect this direction correction proposal for subsequent adjustment of relevant algorithm parameters. Through the previous operation, that is, adjusting the overlap ratio of the stitched image according to the direction correction proposal of LKAS, the purpose is to ensure that the best visualization effect can be obtained every time the vehicle makes a small turn in direction. Even in complex road conditions (such as curves, undulating roads, etc.), clear and stable visualization output can be maintained, allowing the driver to accurately observe the environment around the vehicle and provide better assistance for driving.

[0041] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. An intelligent management method for electronic rearview mirror parameters for reducing blind spots, characterized in that: Including: S101. Automatically identify and adjust the camera's viewing angle range according to the real-time vehicle motion state to compensate for the increased blind area during high-speed driving; S102. Dynamically adjust the sensitivity of the image sensor based on the change of ambient light intensity to optimize the display quality under low-light conditions; S103. Configure the parameters of the picture stitching algorithm according to the driver's personalized vision requirements to achieve adaptive optimization for different driving habits; S104. Set the alarm threshold according to the approaching speed and distance of obstacles to effectively reduce the false trigger and missed alarm rates.

2. The method for intelligently managing parameters of an electronic rearview mirror for reducing blind spots according to claim 1, characterized in that: The method of automatically identifying and adjusting the camera's viewing angle range according to the real-time vehicle motion state includes: Obtain the current vehicle speed V and acceleration A; Calculate the dynamic vision adjustment angle θ according to the formula: θ = arcsin(k*V + b*A), where k and b are coefficients related to speed and acceleration respectively, used to adapt to the optimal field of view at different speeds; If θ is greater than the maximum allowable angle θ_max, set θ to θ_max; Adjust the angle of the left or right camera according to the calculated θ value.

3. The method for intelligently managing parameters of an electronic rearview mirror for reducing blind spots according to claim 1, characterized in that: The further regulation of the sensitivity of the image sensor based on the change of ambient light intensity includes: Obtain the I light flux value of the ambient light intensity; Determine the reference sensitivity S0 and set it as the default parameter of the sensor; Adjust the sensitivity using the formula: S = max(S_min, S0 + m(I - L_ref)), where L_ref is the reference light intensity, m is the sensitivity gain coefficient, and S_min is the lower limit to ensure the minimum display quality; Increase the sensitivity by a preset increment during night driving to cope with darker conditions.

4. The method for intelligently managing parameters of an electronic rearview mirror for reducing blind spots according to claim 3, characterized in that: When the ambient light is too low, adopt an additional near-infrared supplementary lighting system to enhance the camera effect: Obtain the ambient light intensity I and judge I < I_thre; I_thre is the preset ambient light intensity threshold; Turn on the in-vehicle infrared illuminator to increase the effective light brightness; At the same time, increase the image noise filtering level to suppress the formation of noise points; Adjust the supplementary light output power W_T according to the actual working temperature T: W_T = W_base*exp(α*(T - T_ambient)), in this formula, W_base represents the power base under normal temperature conditions, and α is the thermosensitive coefficient.

5. The method for intelligently managing parameters of an electronic rearview mirror for reducing blind spots according to claim 1, characterized in that: Regulate the parameters of the picture stitching algorithm for the driver's personalized vision requirements: Identify the driver's identity and load the preference dataset D through the historical record; Select the corresponding data model M for processing different perspective transformations; Calculate the visual comfort function F(x) = 1 / (1 + β*A)γV^2, x represents an element in the set of user settings, here A represents the angle deviation amount between adjacent frames before and after, and V is the instantaneous vehicle speed, β and γ are influencing factors used to balance accuracy and smoothness; Continuously optimize the collage parameter set P based on the feedback mechanism to optimize the user experience; Predict the image area to be displayed in the future based on driving behavior: Analyze the manipulation input X in multiple consecutive past sample segments; Predict the next key monitoring direction y through machine learning; Establish a custom boundary equation L(x) = wx + b to define the range to be displayed and track it in real time; When the maximum viewing angle ω is exceeded, the proportion of other non-critical parts is compressed to ensure full coverage of safety monitoring. Here, the empirical value of ω is 45°±5%, w depends on the steering wheel rotation arc and b=-65.

6. The method for intelligently managing parameters of an electronic rearview mirror for reducing blind spots according to claim 1, characterized in that: After setting the alarm threshold, execute the corresponding control alarm strategy: Monitor the distance d from the object to the vehicle and the closing rate v_close; Set the basic threshold T_basic and the comprehensive danger level z after weighted average of weights w_dist and w_speed; The decision tree rule is used to determine the final warning level G = G (z) = z / z_high + 7 × sign [(v_rel / d) - τ], z_high represents the critical value, when (v_rel / d) < τ, the speed component is not considered, v_rel represents the relative speed of the two and τ is the theoretical limit, which is used to avoid finding the right position between false alarms and slow reactions; Set graded sound effects and screen color change warning methods according to different scenarios; Add GPS and inertial navigation equipment to read vehicle location information; Calculate the expected collision point c, and update the original evaluation model input x′ according to the vector difference of cl; Compare the instantaneous measurement x′ with the outcomes of similar situations s stored in the database and find the expectation e; e>δ triggers an alarm prompt in advance to improve safety.

7. The method for intelligently managing parameters of an electronic rearview mirror for reducing blind spots according to claim 1, characterized in that: Detecting the front tire steering axis α and the body inclination angle β; Calculate the size of the potential unobserved interval U area S_u caused by yaw; Through the formula S_U=S_default+k*(αβ)*sin((L+w) / d), where L is the basic wheelbase, w is the driving bandwidth, d is the turning radius, and k is the empirical constant factor used to correct the influence of the deformation effect; S_default is the initial area size; Immediately expand to the necessary side wide-angle lens field of view to compensate for the loss and ensure that the surrounding environment is fully visible.

8. The method for intelligently managing parameters of an electronic rearview mirror for reducing blind spots according to claim 1, characterized in that: Automatically adapt relevant algorithm parameters based on the steering assistance service provided by the lane keeping assist system LKAS: When LKAS is enabled, the lateral field of view adjustment unit will be activated simultaneously; Collect the direction correction proposal δ_dir given by LKAS; Adjust the overlap ratio between the stitched images ρ = max((δ_dir)^k, 3), where ρ is directly related to the merged image quality and smoothness; Ensure that every small turn can obtain the best presentation result and maintain clear and stable visual output even in complex road conditions.

9. The method for intelligently managing parameters of an electronic rearview mirror for reducing blind spots according to claim 1, characterized in that: Use radar or laser to detect the road conditions ahead, including but not limited to the slope i and curvature ρ_curva; Combine i,ρ_curva to calculate the new perspective expansion requirement Δθ_new; A non-fixed formula Δθnew=arcos[√{(1(ρ_car / ρ_max)^2)}*cos(i)] is introduced to determine the dynamic change of viewing angle. The parameters in the formula are interpreted as: the maximum tilt value ρ_max under the comfort limit of the cabin and the combined application of the current road curvature description parameter ρ_car and the longitudinal slope index i.

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