Rearview mirror intelligent adjusting method, device and equipment based on driving route and storage medium

Abstract

The invention discloses a rearview mirror intelligent adjusting method, device and equipment based on a driving route and a storage medium, and relates to the technical field of automobile driving control, the rearview mirror intelligent adjusting method based on the driving route comprises the steps that sensor data are acquired, and the sensor data comprise target pose data and driving planning data; obtaining a driving scene signal through a preset scene analysis model and the sensor data; and according to the target rearview mirror angle adjustment model, the driving scene signal and the sensor data, a target adjustment angle of the rearview mirror is obtained, so that the automobile completes rearview mirror angle control according to the target adjustment angle. According to the method, the optimal rearview mirror adjusting angle is intelligently calculated by utilizing the preset scene analysis model and the rearview mirror angle adjusting model through the sensor information, so that a clear and comprehensive view is provided for a driver in various driving scenes, the view blind area is effectively reduced, and the driving safety is improved.

Description

Technical Field

[0001] The present application relates to the field of automobile driving control technology, and in particular to a method, device, equipment and storage medium for intelligently adjusting a rearview mirror based on a driving route. Background Art

[0002] With the development of automobile intelligence, drivers have an increasing demand for driving assistance systems, especially in terms of driving safety. In complex traffic environments, drivers need to understand the conditions around the vehicle in real time. As an important tool for obtaining side and rear vision, the intelligent adjustment of rearview mirrors has become a key requirement for improving driving safety.

[0003] Currently, most car rearview mirrors rely on manual adjustment by the driver, or only have basic automatic adjustment functions, such as automatically adjusting to provide a better view when reversing. These methods usually preset limited adjustment angles to accommodate common scenarios such as normal driving and reversing, but lack the ability to adapt to complex driving environments.

[0004] However, in actual driving, there are many complex scenarios, such as turning, merging into the main road, changing lanes, etc. At this time, the fixed angle of the rearview mirror may not meet the driver's needs for the side and rear vision, resulting in blind spots and increased driving risks. The limitation of the existing technology is that it is impossible to intelligently adjust the angle of the rearview mirror according to the driving route and specific conditions in real time, making it difficult for the driver to obtain the necessary side and rear information in key scenarios, thereby affecting the accurate judgment of the surrounding traffic conditions. Therefore, how to intelligently adjust the angle of the rearview mirror based on the driving route has become an urgent problem to be solved. Summary of the invention

[0005] The purpose of the present application is to provide a method, device, equipment and storage medium for intelligently adjusting a rearview mirror based on a driving route, aiming to solve the technical problem of how to intelligently adjust the angle of a rearview mirror based on a driving route. To achieve the above purpose, the present application proposes a method for intelligently adjusting a rearview mirror based on a driving route, the method comprising:

[0006] Acquiring sensor data, wherein the sensor data includes target posture data and driving planning data;

[0007] Obtaining a driving scene signal through a preset scene analysis model and the sensor data;

[0008] According to the target rearview mirror angle adjustment model, the driving scene signal and the sensor data, the target adjustment angle of the rearview mirror is obtained, so that the automobile completes the rearview mirror angle control according to the target adjustment angle.

[0009] In one embodiment, obtaining a driving scene signal by using a preset scene analysis model and the sensor data includes:

[0010] Obtaining a scene node set according to the driving planning data and a preset scene analysis model;

[0011] Matching the scene node set by the target pose data to obtain a target scene node;

[0012] According to the target scene node, a corresponding driving scene signal is obtained.

[0013] In one embodiment, before obtaining the target scene node by matching the scene node set with the pose data, the method further includes:

[0014] Get the preset distance threshold;

[0015] Obtaining first position data according to the target posture data;

[0016] Combine the first position data and the scene node set to obtain a reference scene node;

[0017] Obtaining a first target distance through the reference scene node and the first position data;

[0018] If the first target distance is greater than or equal to the preset distance threshold, the rearview mirror angle control is exited.

[0019] In one embodiment, before obtaining the target adjustment angle of the rearview mirror according to the target rearview mirror angle adjustment model, the driving scene signal and the sensor data, the method further includes:

[0020] Acquire an initial rearview mirror angle adjustment model and initial data, wherein the initial data includes reference environment data, reference posture data, and reference user sight data;

[0021] Obtaining training data and verification data based on the initial data, wherein the verification data is obtained after the initial data is labeled;

[0022] Obtaining a reference adjustment angle through the initial rearview mirror angle adjustment model and the training data;

[0023] Obtaining a marked adjustment angle based on the verification data;

[0024] Determining a viewing angle loss value according to the reference adjustment angle and the marked adjustment angle;

[0025] The initial rearview mirror angle adjustment model is adjusted according to the angle adjustment parameter and the viewing angle loss value until the initial rearview mirror angle adjustment model converges to obtain a target rearview mirror angle adjustment model.

[0026] In one embodiment, the target adjustment angle of the rearview mirror is obtained according to the target rearview mirror angle adjustment model, the driving scene signal and the sensor data, so that the vehicle completes the mirror angle control according to the target adjustment angle, including:

[0027] Determining a preset adjustment mode according to the driving scene signal;

[0028] Obtaining target user sight data and target environment data through the sensor data;

[0029] Obtaining a target adjustment angle of a rearview mirror according to the target posture data, the target user sight data, the target environment data, and a target rearview mirror angle adjustment model;

[0030] According to the target adjustment angle and the preset adjustment mode, the rear-view mirror angle control is completed.

[0031] In one embodiment, after obtaining the target adjustment angle of the rearview mirror according to the target rearview mirror angle adjustment model, the driving scene signal and the sensor data so that the automobile completes the mirror angle control according to the target adjustment angle, the method further includes:

[0032] Obtaining the target scene node, the second position data, a preset safety distance threshold, and a preset angle;

[0033] Obtaining a second target distance according to the second position data and the target scene node;

[0034] If the second target distance is greater than or equal to the preset safety distance threshold, the rearview mirror is adjusted to the preset angle.

[0035] In one embodiment, after obtaining the second target distance according to the second position data and the target scene node, the method further includes:

[0036] Get the preset adjustment time threshold;

[0037] The time when the target adjustment angle is obtained is obtained and recorded to obtain the target adjustment time;

[0038] If the target adjustment time is greater than or equal to the preset adjustment time threshold, the rearview mirror is adjusted to the preset angle.

[0039] In addition, to achieve the above-mentioned purpose, the present application also proposes a driving route-based intelligent adjustment device for a rearview mirror, the device comprising:

[0040] An acquisition module, used to acquire sensor data, wherein the sensor data includes target posture data and driving planning data;

[0041] An obtaining module, used to obtain a driving scene signal through a preset scene analysis model and the sensor data;

[0042] The completion module is used to obtain the target adjustment angle of the rearview mirror according to the target rearview mirror angle adjustment model, the driving scene signal and the sensor data, so that the car can complete the rearview mirror angle control according to the target adjustment angle.

[0043] In addition, to achieve the above-mentioned purpose, the present application also proposes a rearview mirror intelligent adjustment device based on the driving route, the device comprising: a memory, a processor and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the rearview mirror intelligent adjustment method based on the driving route as described above.

[0044] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the rearview mirror intelligent adjustment method based on the driving route as described above are implemented.

[0045] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the method for intelligent adjustment of rearview mirrors based on driving routes as described above.

[0046] One or more technical solutions proposed in this application have at least the following technical effects:

[0047] This application first collects sensor data to grasp the vehicle's position, posture and upcoming driving path in real time, providing basic information for subsequent driving scene analysis and rearview mirror adjustment. Then, using sensor data and a preset scene analysis model, it can identify the current driving scene, such as turning, merging into a main road, etc., to provide a scene basis for rearview mirror adjustment. Finally, combined with the driving scene signal and sensor data, the rearview mirror angle that best suits the current driving scene is calculated to ensure that the driver has the best side and rear view. This application can intelligently and automatically adjust the rearview mirror angle according to the real-time driving environment to adapt to various complex driving scenes, eliminating the driver's field of vision blind spots caused by improper rearview mirror angles in complex traffic environments, ensuring a clear rear view in all situations, and reducing the risk of accidents caused by insufficient vision, thereby improving driving safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0049] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0050] Figure 1 A flowchart diagram of a first embodiment of a method for intelligently adjusting a rearview mirror based on a driving route provided in the present application;

[0051] Figure 2 A flow chart of the second embodiment of the intelligent adjustment method of the rearview mirror based on the driving route provided in the present application;

[0052] Figure 3 This is a schematic diagram of the module structure of the intelligent adjustment device for rearview mirror based on the driving route according to an embodiment of the present application;

[0053] Figure 4 This is a schematic diagram of the device structure of the hardware operating environment involved in the method for intelligently adjusting a rearview mirror based on a driving route in an embodiment of the present application.

[0054] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0055] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0056] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0057] With the development of automobile intelligence, drivers' demand for driving assistance systems is growing, especially in terms of driving safety. In a complex traffic environment, drivers need to understand the situation around the vehicle in real time. As an important tool for obtaining the side and rear view, the intelligent adjustment of the rearview mirror has become a key requirement for improving driving safety. At present, the adjustment of the rearview mirror of most cars depends on manual operation by the driver, or only has basic automatic adjustment functions, such as automatic adjustment to provide a better view when reversing. These methods usually preset limited adjustment angles to adapt to common scenarios such as normal driving and reversing, but lack the ability to adapt to complex driving environments. However, in actual driving, there are many complex scenarios, such as turning, merging into the main road, changing lanes, etc. At this time, the fixed angle of the rearview mirror may not meet the driver's demand for the side and rear view, resulting in blind spots and increased driving risks. The limitation of the existing technology is that it is impossible to intelligently adjust the angle of the rearview mirror according to the driving route and specific conditions in real time, making it difficult for the driver to obtain the necessary side and rear information in key scenarios, thereby affecting the accurate judgment of the surrounding traffic conditions.

[0058] The main solution of the embodiment of the present application is: the embodiment first collects sensor data to grasp the position, posture and upcoming driving path of the vehicle in real time, and provides basic information for subsequent driving scene analysis and rearview mirror adjustment. Then, by using sensor data and a preset scene analysis model, the current driving scene can be identified, such as turning, merging into a main road, etc., so as to provide a scene basis for rearview mirror adjustment. Finally, the driving scene signal and sensor data are combined to calculate the rearview mirror angle that best suits the current driving scene to ensure that the driver obtains the best side and rear view. The embodiment can automatically adjust the rearview mirror angle intelligently according to the real-time driving environment to adapt to various complex driving scenes, eliminate the driver's field of vision blind spots caused by improper rearview mirror angles in complex traffic environments, ensure a clear rear view in all situations, reduce the risk of accidents caused by insufficient field of vision, and thus improve driving safety.

[0059] It should be noted that the execution subject of the embodiment of the present application may be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, an electronic control unit, etc. The following takes the electronic control unit as an example to illustrate this embodiment and the following embodiments.

[0060] Based on this, the embodiment of the present application provides a method for intelligently adjusting a rearview mirror based on a driving route, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the method for intelligently adjusting a rearview mirror based on a driving route of the present application.

[0061] In this embodiment, the rearview mirror intelligent adjustment method based on the driving route includes steps S10 to S30:

[0062] Step S10, acquiring sensor data, wherein the sensor data includes target posture data and driving planning data;

[0063] It should be noted that sensor data can be data collected and measured by sensing devices. These data can be real-time status data, indicating the operating status at a certain moment, such as speed, power, etc.; or accumulated data, indicating the accumulated amount of data within a certain range, such as mileage, heat consumption, etc. In the field of automotive intelligence, sensor data are particularly important because they provide the car with information about its operating status and environment, which is essential for realizing functions such as autonomous driving and safety monitoring. Target pose data can be data that describes the position and pose of an object in space (including three dimensions of position and three dimensions of rotation, the so-called "six-dimensional pose"). In an autonomous vehicle, this may include information such as the vehicle's global positioning system (GPS) coordinates, the vehicle's position relative to the surrounding environment, and the vehicle's direction and tilt angle. Driving planning data involves information such as the vehicle's driving path, speed, acceleration, etc. These data are usually generated by the vehicle's navigation system and driving assistance system to guide the vehicle to drive safely and efficiently along the established route. Driving planning data can include path planning solutions, estimated arrival time, traffic conditions information, etc., to help vehicles make decisions in complex traffic environments. Combining the above information, sensor data, target posture data and driving planning data together provide the car with comprehensive state and environmental perception capabilities, enabling the car to make precise control decisions based on real-time data to adapt to different driving scenarios and improve driving safety.

[0064] It is understandable that the step of obtaining sensor data involves collecting real-time information of the vehicle, which together provide a decision basis for the intelligent adjustment of the rearview mirror system, enabling it to intelligently adjust the rearview mirror angle according to the actual driving conditions of the vehicle and the surrounding environment to optimize the driver's field of view.

[0065] Step S20, obtaining a driving scene signal through a preset scene analysis model and the sensor data;

[0066] It should be noted that the preset scene analysis model can be an analysis model based on specific scene features and rules, which can intelligently analyze and understand the vehicle's driving environment. This model integrates deep learning algorithms, data mining technology, and multiple sensor data fusion technologies to identify and understand scene elements such as traffic signals, road signs, and construction areas. The driving scene signal can be a signal obtained by analyzing the sensor data through the model, describing the specific driving environment and conditions of the vehicle, such as whether the vehicle is approaching an intersection, whether it is in a turning state, and whether the speed needs to be adjusted. These signals are key inputs for vehicle intelligent decision-making and control, such as automatic adjustment of rearview mirrors and speed control. They enable vehicles to adapt to complex traffic environments more intelligently and improve driving safety and efficiency.

[0067] It can be understood that by using a pre-defined analysis model and combining the vehicle's real-time sensor data (such as position, speed, steering angle, etc.), the vehicle's current specific driving environment and status (such as going straight, turning, approaching an intersection, etc.) can be intelligently identified and judged, and this information can be converted into driving scene signals, providing an accurate basis for subsequent vehicle control decisions (such as rearview mirror adjustment, vehicle speed adjustment, etc.).

[0068] As an example, a driving scene signal is obtained through a preset scene analysis model and the sensor data, including: obtaining a scene node set according to the driving planning data and the preset scene analysis model; obtaining a target scene node by matching the scene node set with the target pose data; and obtaining a corresponding driving scene signal according to the target scene node.

[0069] Among them, the scene node set can be a series of key points or condition sets defined in the preset scene analysis model, which represent various specific environments or events that may be encountered during driving, such as intersections, turning points, and confluence points. These nodes are the basis for model recognition and analysis of driving scenes, and are used to divide complex driving environments into recognizable and operable units. The target scene node refers to a specific node matched from the scene node set, which is determined after comparing and analyzing the vehicle's current target posture data (such as the vehicle's real-time position, speed, direction, etc.) with the scene node set. This node is directly associated with the specific driving scene that the vehicle is about to enter or is experiencing, such as the intersection the vehicle is about to arrive at or the curve it is passing. The driving scene signal can be a signal derived from the target scene node, which describes the specific driving scene in which the vehicle is located in detail, and provides the vehicle's intelligent system with necessary information for corresponding operations and decisions, such as adjusting the rearview mirror angle, changing the vehicle speed, and issuing warnings. This signal is the key for the vehicle's intelligent control system to respond to changes in the external environment, ensuring that the vehicle can drive safely and efficiently.

[0070] Specifically, first, a series of possible driving scene nodes, namely, a scene node set, are determined based on the vehicle's driving planning data and the built-in preset scene analysis model; then, the vehicle's real-time target posture data is used to match these scene nodes to identify the specific scene node that the vehicle is about to enter or is currently in, namely, the target scene node; finally, based on this target scene node, a corresponding driving scene signal is generated. This signal describes the vehicle's specific driving environment and status in detail, providing an accurate basis for subsequent driving decisions and control.

[0071] As an example, before obtaining the target scene node by matching the scene node set with the posture data, it also includes: obtaining a preset distance threshold; obtaining first position data based on the target posture data; obtaining a reference scene node by combining the first position data and the scene node set; obtaining a first target distance through the reference scene node and the first position data; if the first target distance is greater than or equal to the preset distance threshold, exiting the rearview mirror angle control.

[0072] Among them, the preset distance threshold can be a distance parameter set in advance, which is used to determine the critical distance of whether the rearview mirror angle needs to be adjusted. When the distance between the vehicle and a specific scene node (such as a turning point, a confluence point, etc.) reaches or exceeds this threshold, it will be determined whether the rearview mirror angle needs to be adjusted. The first position data can be the position information calculated by the vehicle based on the target posture data, which usually includes the current position coordinates (such as longitude and latitude), speed, heading, etc. of the vehicle, which are used to determine the specific position of the vehicle in space. The reference scene node can be a specific node related to the current position of the vehicle selected from the scene node set. By comparing the first position data with the scene node set, it can be determined which scene nodes are closest to or related to the current position of the vehicle, and these nodes are the reference scene nodes. The first target distance can be the distance between the current position of the vehicle (the first position data) and the reference scene node. By calculating this distance, the proximity of the vehicle to the upcoming driving event (such as turning, confluence, etc.) can be determined.

[0073] Specifically, first, a preset distance threshold is set, and then the current position of the vehicle, i.e., the first position data, is determined based on the target posture data of the vehicle; then, this position data is matched with the scene node set to find the reference scene node that is most relevant to the vehicle position; then, the distance between the vehicle and these reference scene nodes is calculated, i.e., the first target distance; finally, if this first target distance is greater than or equal to the preset distance threshold, it will be decided not to adjust the rearview mirror angle, i.e., exit the rearview mirror angle control process, so as to avoid unnecessary rearview mirror adjustments when the vehicle is too far away from the key driving scene nodes.

[0074] As an example, before obtaining the target adjustment angle of the rearview mirror according to the target rearview mirror angle adjustment model, the driving scene signal and the sensor data, it also includes: acquiring an initial rearview mirror angle adjustment model and initial data, the initial data including reference environment data, reference posture data and reference user line of sight data; obtaining training data and verification data based on the initial data, the verification data being obtained after the initial data is labeled; obtaining a reference adjustment angle through the initial rearview mirror angle adjustment model and the training data; obtaining a labeled adjustment angle based on the verification data; determining a viewing angle loss value based on the reference adjustment angle and the labeled adjustment angle; adjusting the initial rearview mirror angle adjustment model according to the angle adjustment parameter and the viewing angle loss value until the initial rearview mirror angle adjustment model converges to obtain a target rearview mirror angle adjustment model.

[0075] Among them, the initial rearview mirror angle adjustment model is a system based on machine learning, and its basic architecture usually includes a data input layer, a feature extraction layer, a model core layer, and an output layer. In the data input layer, the model receives initial data, including reference environment data, reference posture data, and reference user line of sight data. These data are used to describe the surrounding environment of the vehicle, the position and posture of the vehicle, and the driver's line of sight. The feature extraction layer is responsible for identifying and extracting key features from the input data, which are essential for predicting the optimal adjustment angle of the rearview mirror. The model core layer usually contains multiple hidden layers, such as multi-layer perceptrons or convolutional layers in deep learning models, which are used to learn the complex mapping relationship between features and rearview mirror angle adjustment. Finally, the output layer generates a predicted reference adjustment angle, that is, the rearview mirror angle predicted by the model based on the input features. The entire model is trained with training data. During the training process, the model parameters are adjusted to minimize the view loss value between the predicted angle and the annotated adjustment angle in the verification data until the model converges and can accurately predict the rearview mirror angle under various conditions to form a target rearview mirror angle adjustment model. The initial data can include three types of data for training and adjusting the initial model. Reference environment data can be used to describe information about the vehicle's surroundings, such as road type, traffic conditions, etc. Reference pose data can describe the vehicle's position and posture information, such as the vehicle's latitude and longitude, heading, tilt angle, etc. Reference user sight data can be used to describe the driver's sight direction and focus data, which is used to understand the driver's visual needs. Training data can be a dataset extracted from the initial data and used to train the initial rearview mirror angle adjustment model to help the model learn how to predict the best rearview mirror angle. Verification data can be a dataset extracted from the initial data and used to verify the accuracy of the model's prediction. These data are annotated, that is, the correct rearview mirror angle is known. The reference adjustment angle can be a predicted angle calculated by the initial rearview mirror angle adjustment model and training data, which is used to compare with the actual annotated angle. The annotated adjustment angle can be the known correct rearview mirror angle in the verification data, which is obtained by manual annotation or other precise measurement methods. The view loss value can be the difference between the reference adjustment angle and the annotated adjustment angle, which is used to evaluate the accuracy of the model's prediction. Angle adjustment parameters can be parameters that adjust the model to improve its prediction performance, which may include learning rate, weight decay, etc. The target rearview mirror angle adjustment model may be a final model obtained by convergence after multiple iterations and adjustments, and may accurately predict the optimal adjustment angle of the rearview mirror under different conditions.

[0076] Specifically, first, collect initial data, including information such as the environment, vehicle posture, and user line of sight. Then separate the training set and validation set from these data, where the validation set data is accurate angle data that has been manually annotated. Then use the training set data to train the initial rearview mirror angle adjustment model and generate reference adjustment angles. These angles are then compared with the annotated adjustment angles in the validation set to calculate the perspective loss value, that is, the difference between the model prediction value and the actual value. Finally, based on this loss value and some adjustment parameters, the model is iteratively optimized until the model can accurately predict the optimal angle of the rearview mirror. At this time, the initial model converges to the target rearview mirror angle adjustment model, which can be used for actual rearview mirror automatic adjustment.

[0077] Step S30, obtaining a target adjustment angle of the rearview mirror according to the target rearview mirror angle adjustment model, the driving scene signal and the sensor data, so that the automobile completes rearview mirror angle control according to the target adjustment angle.

[0078] It should be noted that the target adjustment angle can be the optimal angle to which the rearview mirror should be adjusted, calculated based on the target rearview mirror angle adjustment model, the current driving scene signal, and real-time sensor data. This angle is based on a comprehensive analysis of the vehicle's environment and driving status, with the goal of providing the driver with the best side and rear view, ensuring a clear and comprehensive view in various driving scenarios, so as to improve driving safety.

[0079] It is understandable that the target mirror angle adjustment model is used, which is an algorithm or mathematical model that is trained to accurately calculate the ideal mirror position based on various input data, combined with real-time driving scene signals and sensor data, such as vehicle speed, steering angle, road conditions, etc., to dynamically calculate the target angle to which the mirror should be adjusted. Subsequently, the car will automatically adjust the mirror according to this calculated target adjustment angle to ensure that the driver can obtain the best side rear view in various driving scenarios, thereby improving driving safety and convenience.

[0080] As an example, after obtaining the target adjustment angle of the rearview mirror according to the target rearview mirror angle adjustment model, the driving scene signal and the sensor data so that the car completes the rearview mirror angle control according to the target adjustment angle, it also includes: obtaining a target scene node, second position data, a preset safety distance threshold and a preset angle; obtaining a second target distance according to the second position data and the target scene node; if the second target distance is greater than or equal to the preset safety distance threshold, adjusting the rearview mirror to the preset angle.

[0081] Among them, the second position data can be the current position information of the vehicle after passing the target scene node, which includes the GPS coordinates, speed, direction, etc. of the vehicle, which is used to determine the specific position of the vehicle in space. The preset safety distance threshold can be a pre-set distance value, which is used to determine the minimum safety distance that the vehicle should maintain from the target scene node after passing the target scene node. When the distance between the vehicle and the target scene node is greater than or equal to this threshold, it is considered that the vehicle is far enough away from the node and specific operations are required. The preset angle can be a specific angle to which the rearview mirror should be adjusted. This angle is determined based on the user's normal driving on a straight road, and is used to provide the best field of view when the vehicle leaves the target scene node. The second target distance can be the actual distance between the current position of the vehicle (second position data) and the target scene node. This distance is calculated to determine whether the vehicle has exited the preset safety distance threshold range.

[0082] Specifically, first obtain the position of the target scene node, the current position of the vehicle (second position data), the preset safety distance threshold, and the preset angle to which the rearview mirror should be adjusted. Then, calculate the actual distance between the vehicle and the target scene node (second target distance). If this distance reaches or exceeds the preset safety distance threshold, indicating that the vehicle has exited the critical driving scene, the rearview mirror will be automatically adjusted to the preset angle to ensure that the driver returns the rearview mirror in time after passing the critical driving scene, thereby improving driving safety.

[0083] As an example, after obtaining the second target distance according to the second position data and the target scene node, it also includes: obtaining a preset adjustment time threshold; obtaining the time when the target adjustment angle is obtained and recording it to obtain the target adjustment time; if the target adjustment time is greater than or equal to the preset adjustment time threshold, adjusting the rearview mirror to the preset angle.

[0084] Among them, the preset adjustment time threshold can be a pre-set time parameter used to determine the time limit of the rearview mirror adjustment operation. It defines the maximum time range allowed when adjusting the rearview mirror angle. If the time of the adjustment operation exceeds this threshold, it may be considered that the adjustment is no longer valid or needs to be re-evaluated. The target adjustment time can be the actual time taken from the start of the adjustment to the target adjustment angle when adjusting the rearview mirror angle. This time record is used to compare with the preset adjustment time threshold to determine whether the adjustment operation is completed within a reasonable time range. By comparing the target adjustment time with the preset adjustment time threshold, it can be decided whether to continue adjusting the rearview mirror to the preset angle to ensure the effectiveness and timeliness of the adjustment process.

[0085] Specifically, a preset adjustment time threshold is set, i.e., the maximum time allowed for the rearview mirror to be adjusted to the target angle. During the rearview mirror adjustment process, the time taken from the start of adjustment to reaching the target angle is recorded, i.e., the target adjustment time. If the target adjustment time exceeds the preset threshold, i.e., the adjustment process takes too long, it may be due to hardware failure or other problems that prevent the adjustment from being completed in time, and the rearview mirror will be automatically restored to the preset safety angle to ensure that the driver's vision is not disturbed under normal driving conditions, ensuring driving safety.

[0086] This embodiment provides a method for intelligently adjusting rearview mirrors based on driving routes. This embodiment first collects sensor data to grasp the position, posture and driving route of the vehicle in real time, and provides basic information for subsequent driving scene analysis and rearview mirror adjustment. Then, by using sensor data and a preset scene analysis model, the current driving scene can be identified, such as turning, merging into a main road, etc., so as to provide a scene basis for rearview mirror adjustment. Finally, the driving scene signal and sensor data are combined to calculate the rearview mirror angle that best suits the current driving scene to ensure that the driver obtains the best side and rear view. This embodiment can automatically adjust the rearview mirror angle intelligently according to the real-time driving environment to adapt to various complex driving scenes, eliminate the blind spots of the driver's vision caused by improper rearview mirror angles in complex traffic environments, ensure a clear rear view in all situations, reduce the risk of accidents caused by insufficient vision, and thus improve driving safety.

[0087] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can refer to the above introduction, and will not be repeated later. Figure 2 , Figure 2 This is a flow chart of the second embodiment of the method for intelligently adjusting a rearview mirror based on a driving route of the present application. Step S30 of the method for intelligently adjusting a rearview mirror based on a driving route includes steps S31 to S34:

[0088] Step S31, determining a preset adjustment mode according to the driving scene signal;

[0089] It should be noted that the preset adjustment mode can be a series of parameters and operations preset according to specific driving scene signals, and these parameters and operations are configured to be automatically executed when a specific scene is detected to optimize the performance and safety of the vehicle.

[0090] It is understood that by identifying the current driving scene signals and automatically selecting a pre-set adjustment mode according to these signals. This preset adjustment mode is a series of parameters configured for a specific driving scene (such as straight driving, turning, reversing, etc.). This adjustment mode may include the speed of rearview mirror angle control and the opening of the anti-glare function to ensure the best driving vision and safety in different driving situations.

[0091] Step S32, obtaining target user sight data and target environment data through the sensor data;

[0092] It should be noted that the target user's line of sight data involves line of sight estimation technology, which infers the point or direction the driver is looking at by analyzing the driver's facial features, especially the position and direction of the eyes. This data is crucial for applications such as in-vehicle interactive systems and driver attention monitoring systems, and can help the system better understand the driver's intentions and needs. At the same time, it can also obtain the line of sight height of different users to provide data support for subsequent adjustment of the target angle. The target environment data can include various information about the vehicle's surroundings, such as the position and speed of other vehicles, pedestrians, and obstacles. This data is usually collected by on-board sensors such as cameras, radars, and laser radars (LiDAR). Environmental data can be used to support automatic adjustment of rearview mirrors.

[0093] It is understandable that the data collected by sensors installed on the vehicle, such as cameras, infrared sensors, eye tracking devices, etc., are used to analyze and determine the driver's line of sight direction and height (target user line of sight data), as well as the state of the vehicle's surroundings (target environment data), including other vehicles, pedestrians, road signs and obstacles. These data help the vehicle's intelligent system understand the driver's visual focus and external environment conditions so that it can make corresponding adjustments and responses, such as adjusting the rearview mirror angle or taking obstacle avoidance measures to improve driving safety and comfort.

[0094] Step S33, obtaining a target adjustment angle of the rearview mirror according to the target posture data, the target user sight data, the target environment data, and a target rearview mirror angle adjustment model;

[0095] It can be understood that by comprehensively considering the target posture data of the vehicle (including the position, speed and direction of the vehicle), the target user line of sight data (the driver's line of sight direction and focus), the target environment data (information about the vehicle's surroundings, such as the position of other vehicles, pedestrians and obstacles), and a target rearview mirror angle adjustment model. Through these inputs, the specific angle to which the rearview mirror should be adjusted can be calculated to ensure that the driver can obtain the best side and rear view in various driving scenarios, thereby improving driving safety and driving experience.

[0096] Step S34, completing the rearview mirror angle control according to the target adjustment angle and the preset adjustment mode.

[0097] It is understood that after the target adjustment angle is determined, the actual angle adjustment of the rearview mirror will be performed based on this angle and the preset adjustment mode. The preset adjustment mode may include a series of operating procedures or strategies designed to cope with specific driving scenarios. The rearview mirror will be automatically adjusted to the calculated target angle, or fine-tuned according to the parameters and rules in the preset mode to ensure that the angle of the rearview mirror can meet the needs of the current driving scenario and meet the standards of safety and comfort.

[0098] This embodiment first analyzes the driving scene signal to identify the current driving environment (such as straight driving, turning, parking, etc.) and selects a preset adjustment mode that best suits the environment. This mode contains rearview mirror adjustment parameters optimized for specific scenarios to ensure that the rearview mirror can respond quickly and adapt to different driving conditions. Then, by obtaining the target user's line of sight data and the target environment data, the target adjustment angle is calculated, and finally, according to the calculated target adjustment angle and the preset adjustment mode, the rearview mirror is automatically adjusted to the optimal position. This step ensures that the angle of the rearview mirror can adapt to changes in the driving environment in real time and provide the driver with the best side and rear view. This embodiment reduces the need for drivers to manually adjust the rearview mirror, allowing them to focus more on driving and improving driving safety. At the same time, comfort is also improved because the driver can always get the best view regardless of how the driving conditions change.

[0099] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the method for intelligent adjustment of rearview mirrors based on driving routes of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0100] This application also provides a rearview mirror intelligent adjustment device based on the driving route, please refer to Figure 3 , the intelligent adjustment device for rearview mirror based on driving route comprises:

[0101] An acquisition module 10 is used to acquire sensor data, wherein the sensor data includes target posture data and driving planning data;

[0102] An obtaining module 20 is used to obtain a driving scene signal through a preset scene analysis model and the sensor data;

[0103] The completion module 30 is used to obtain the target adjustment angle of the rearview mirror according to the target rearview mirror angle adjustment model, the driving scene signal and the sensor data, so that the car can complete the rearview mirror angle control according to the target adjustment angle.

[0104] The intelligent adjustment device for rearview mirrors based on driving routes provided by the present application adopts the intelligent adjustment method for rearview mirrors based on driving routes in the above-mentioned embodiments, and can solve the technical problem of how to intelligently adjust the angle of the rearview mirrors based on driving routes. Compared with the prior art, the beneficial effects of the intelligent adjustment device for rearview mirrors based on driving routes provided by the present application are the same as the beneficial effects of the intelligent adjustment method for rearview mirrors based on driving routes provided by the above-mentioned embodiments, and the other technical features of the intelligent adjustment device for rearview mirrors based on driving routes are the same as the features disclosed in the above-mentioned embodiments, and will not be described in detail here.

[0105] The present application provides a driving route-based intelligent adjustment device for a rearview mirror, the driving route-based intelligent adjustment device for a rearview mirror comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the driving route-based intelligent adjustment method for a rearview mirror in the above-mentioned embodiment one.

[0106] Reference below Figure 4 , which shows a schematic diagram of the structure of the intelligent adjustment device for rearview mirrors based on driving routes suitable for implementing the embodiment of the present application. The intelligent adjustment device for rearview mirrors based on driving routes in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 4 The driving route-based intelligent adjustment device for rearview mirrors shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0107] like Figure 4As shown, the intelligent adjustment device for the rearview mirror based on the driving route may include a processing device 1001 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM: Read Only Memory) 1002 or the program loaded from the storage device 1003 to the random access memory (RAM: Random Access Memory) 1004. In the RAM 1004, various programs and data required for the operation of the intelligent adjustment device for the rearview mirror based on the driving route are also stored. The processing device 1001, the ROM 1002 and the RAM 1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 can allow the intelligent adjustment device for the rearview mirror based on the driving route to communicate wirelessly or wired with other devices to exchange data. Although the figure shows the intelligent adjustment device for the rearview mirror based on the driving route with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have alternatively.

[0108] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0109] The intelligent adjustment device for rearview mirrors based on driving routes provided by the present application adopts the intelligent adjustment method for rearview mirrors based on driving routes in the above-mentioned embodiments, and can solve the technical problem of how to intelligently adjust the angle of rearview mirrors based on driving routes. Compared with the prior art, the beneficial effects of the intelligent adjustment device for rearview mirrors based on driving routes provided by the present application are the same as the beneficial effects of the intelligent adjustment method for rearview mirrors based on driving routes provided by the above-mentioned embodiments, and the other technical features of the intelligent adjustment device for rearview mirrors based on driving routes are the same as the features disclosed in the method of the above-mentioned embodiment, and will not be described in detail here.

[0110] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0111] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0112] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the method for intelligently adjusting a rearview mirror based on a driving route in the above-mentioned embodiment.

[0113] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer 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 of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0114] The computer-readable storage medium may be included in the intelligent adjustment device for rearview mirrors based on the driving route; or may exist independently without being assembled into the intelligent adjustment device for rearview mirrors based on the driving route.

[0115] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the intelligent adjustment device for rearview mirrors based on the driving route, the intelligent adjustment device for rearview mirrors based on the driving route: obtains sensor data, wherein the sensor data includes target posture data and driving planning data; obtains a driving scene signal through a preset scene analysis model and the sensor data; obtains a target adjustment angle of the rearview mirror according to a target rearview mirror angle adjustment model, the driving scene signal and the sensor data, so that the car completes rearview mirror angle control according to the target adjustment angle.

[0116] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0117] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0118] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0119] The readable storage medium provided by the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned method for intelligently adjusting the rearview mirror based on the driving route, and can solve the technical problem of how to intelligently adjust the angle of the rearview mirror based on the driving route. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as the beneficial effects of the method for intelligently adjusting the rearview mirror based on the driving route provided by the above-mentioned embodiment, and will not be described in detail here.

[0120] The present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for intelligent adjustment of rearview mirrors based on driving routes.

[0121] The computer program product provided by the present application can solve the technical problem of how to intelligently adjust the angle of the rearview mirror based on the driving route. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as the beneficial effects of the method for intelligently adjusting the rearview mirror based on the driving route provided by the above embodiment, and will not be described in detail here.

[0122] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for intelligently adjusting a rearview mirror based on a driving route, characterized in that: The method comprises: Acquiring sensor data, wherein the sensor data includes target posture data and driving planning data; Obtaining a driving scene signal through a preset scene analysis model and the sensor data; According to the target rearview mirror angle adjustment model, the driving scene signal and the sensor data, the target adjustment angle of the rearview mirror is obtained, so that the automobile completes the rearview mirror angle control according to the target adjustment angle.

2. The method according to claim 1, characterized in that The obtaining of the driving scene signal by using the preset scene analysis model and the sensor data includes: Obtaining a scene node set according to the driving planning data and a preset scene analysis model; Matching the scene node set by the target pose data to obtain a target scene node; According to the target scene node, a corresponding driving scene signal is obtained.

3. The method according to claim 2, characterized in that Before obtaining the target scene node by matching the scene node set with the pose data, the method further includes: Get the preset distance threshold; Obtaining first position data according to the target posture data; Combine the first position data and the scene node set to obtain a reference scene node; Obtaining a first target distance through the reference scene node and the first position data; If the first target distance is greater than or equal to the preset distance threshold, the rearview mirror angle control is exited.

4. The method according to claim 1, characterized in that Before obtaining the target adjustment angle of the rearview mirror according to the target rearview mirror angle adjustment model, the driving scene signal and the sensor data, the method further includes: Acquire an initial rearview mirror angle adjustment model and initial data, wherein the initial data includes reference environment data, reference posture data, and reference user sight data; Obtaining training data and verification data based on the initial data, wherein the verification data is obtained after the initial data is labeled; Obtaining a reference adjustment angle through the initial rearview mirror angle adjustment model and the training data; Obtaining a marked adjustment angle based on the verification data; Determining a viewing angle loss value according to the reference adjustment angle and the marked adjustment angle; The initial rearview mirror angle adjustment model is adjusted according to the angle adjustment parameter and the viewing angle loss value until the initial rearview mirror angle adjustment model converges to obtain a target rearview mirror angle adjustment model.

5. The method according to claim 1, characterized in that The step of obtaining the target adjustment angle of the rearview mirror according to the target rearview mirror angle adjustment model, the driving scene signal and the sensor data, so that the vehicle completes the mirror angle control according to the target adjustment angle, comprises: Determining a preset adjustment mode according to the driving scene signal; Obtaining target user sight data and target environment data through the sensor data; Obtaining a target adjustment angle of a rearview mirror according to the target posture data, the target user sight data, the target environment data, and a target rearview mirror angle adjustment model; According to the target adjustment angle and the preset adjustment mode, the rear-view mirror angle control is completed.

6. The method according to claim 1, characterized in that After obtaining the target adjustment angle of the rearview mirror according to the target rearview mirror angle adjustment model, the driving scene signal and the sensor data so that the automobile completes the mirror angle control according to the target adjustment angle, the method further includes: Obtaining the target scene node, the second position data, a preset safety distance threshold, and a preset angle; Obtaining a second target distance according to the second position data and the target scene node; If the second target distance is greater than or equal to the preset safety distance threshold, the rearview mirror is adjusted to the preset angle.

7. The method according to claim 6, characterized in that After obtaining the second target distance according to the second position data and the target scene node, the method further includes: Get the preset adjustment time threshold; The time when the target adjustment angle is obtained is obtained and recorded to obtain the target adjustment time; If the target adjustment time is greater than or equal to the preset adjustment time threshold, the rearview mirror is adjusted to the preset angle.

8. An intelligent rearview mirror adjustment device based on driving route, characterized in that: The device comprises: An acquisition module, used to acquire sensor data, wherein the sensor data includes target posture data and driving planning data; An obtaining module, used to obtain a driving scene signal through a preset scene analysis model and the sensor data; The completion module is used to obtain the target adjustment angle of the rearview mirror according to the target rearview mirror angle adjustment model, the driving scene signal and the sensor data, so that the car can complete the rearview mirror angle control according to the target adjustment angle.

9. An intelligent rearview mirror adjustment device based on a driving route, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the method for intelligently adjusting a rearview mirror based on a driving route as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the method for intelligently adjusting a rearview mirror based on a driving route as described in any one of claims 1 to 7 are implemented.

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