Vehicle headlamp control method and device and storage medium

Abstract

The invention provides a vehicle headlamp control method and device and a storage medium, and the method comprises the steps: obtaining a predicted driving track of a vehicle according to inertial navigation data and driving map information of the vehicle; on the basis of the predicted driving track of the vehicle, the behavior intention prediction information of the vehicle and the behavior intention prediction information of other users on the road, corresponding quality influence weight ratio parameters are distributed to different visual feature parameters contained in the visual data collected by the vehicle; determining the quality evaluation information of the visual data collected by the vehicle based on the quality influence weight ratio parameter; when it is determined that the quality evaluation information is lower than the preset quality, the vehicle headlight is controlled and adjusted according to the quality evaluation information, and control adjustment at least comprises one of brightness adjustment, low beam or high beam adjustment and irradiation field angle adjustment on the vehicle headlight. Therefore, the visual data of the area irradiated by the headlamp can meet the operation requirement of the vehicle on the sensing data, and the driving safety and the driving experience of a user are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent driving, and in particular to a control method and device for vehicle headlamps and a storage medium. BACKGROUND

[0002] With the development of intelligent vehicles, users have higher requirements for the automation and intelligence of vehicle control. However, the traditional intelligent driving vehicles in the industry still adjust and control the headlamp illumination area according to the recognized driving habits, which makes it difficult to better meet the high standard requirements of the perception data of the road conditions and environment of the intelligent driving vehicle based on the traditional headlamp illumination area. The auxiliary effect of the intelligent driving vehicle is insufficient, resulting in poor user experience. Therefore, how to provide a vehicle headlamp control method that can meet the high requirements of intelligent driving vehicles for road condition and environment perception data, so that the controlled vehicle headlamps can provide appropriate illumination area control according to the driving environment, to provide better driving assistance for intelligent driving vehicles, and improve the driving safety and user experience of intelligent driving vehicles, has become an important problem that needs to be solved in the industry. SUMMARY

[0003] Therefore, the embodiments of the present application provide a control method and device for vehicle headlamps, a storage medium and an electronic device to at least or partially solve the above problems.

[0004] In a first aspect, the embodiments of the present application provide a control method for vehicle headlamps, comprising:

[0005] obtaining a predicted driving trajectory of the vehicle according to inertial navigation data and driving map information of the vehicle;

[0006] assigning a corresponding quality influence weight ratio parameter to different visual feature parameters contained in the visual data collected by the vehicle based on the predicted driving trajectory of the vehicle, behavior intention prediction information of the vehicle, and behavior intention prediction information of other road users;

[0007] determining quality evaluation information of the visual data collected by the vehicle based on the quality influence weight ratio parameter;

[0008] when it is determined that the quality evaluation information is lower than a preset quality, controlling and adjusting the vehicle headlamps according to the quality evaluation information, wherein the control and adjustment at least includes one of the following: adjusting the light and dark degree of the vehicle headlamps, adjusting the low beam or high beam, and adjusting the illumination field angle.

[0009] Optionally, in an embodiment of the present application, the quality influence weight ratio parameters are assigned to different visual feature parameters contained in the visual data of the host vehicle based on the predicted driving trajectory of the host vehicle, the behavior intention prediction information of the host vehicle and the behavior intention prediction information of other road users, including:

[0010] The visual data is divided into grid regions;

[0011] The visual feature parameters of the visual data corresponding to each grid region are determined, and the visual feature parameters at least include one feature parameter value of brightness, contrast and definition of the visual image;

[0012] The quality influence weight ratio parameters are assigned to different visual feature parameters of each grid region based on the predicted driving trajectory of the host vehicle, the behavior intention prediction information of the host vehicle and the behavior intention prediction information of other road users;

[0013] According to the quality influence weight ratio parameters, the feature weighted average value of each grid region is calculated, and the feature weighted average value is taken as the quality evaluation information of the visual data.

[0014] Optionally, in an embodiment of the present application, when the quality evaluation information is determined to be lower than the preset quality, the vehicle headlamp is controlled and adjusted according to the quality evaluation information, including:

[0015] The quality influence weight ratio parameters of the current driving stage are fixed;

[0016] The quality evaluation information of the visual data collected by the host vehicle after the control and adjustment is recalculated based on the fixed quality influence weight ratio parameters;

[0017] When the recalculated quality evaluation information is greater than or equal to the preset quality, the control and adjustment of the vehicle headlamp according to the quality evaluation information is stopped.

[0018] Optionally, in an embodiment of the present application, the visual data is divided into grid regions, including:

[0019] The illumination field of each headlamp of the host vehicle is determined;

[0020] The visual data is divided into grid regions from near to far based on the illumination field and the shooting area of the vehicle-mounted camera.

[0021] Optionally, in an embodiment of the present application, when the corresponding quality impact weight ratio parameters are assigned to the different visual feature parameters of each grid area, the quality impact weight ratio assigned to the grid area close to the headlamp of the vehicle body is greater than the quality impact weight ratio assigned to the grid area far from the headlamp of the vehicle body, and the quality impact weight ratio assigned to the grid area where the illumination fields of the multiple headlamps overlap is greater than the quality impact weight ratio assigned to the grid area where the illumination fields do not overlap.

[0022] Optionally, in an embodiment of the present application, before the corresponding quality impact weight ratio parameters are assigned to the different visual feature parameters contained in the visual data of the vehicle based on the driving trajectory of the vehicle, the behavior intention prediction information of the vehicle, and the behavior intention prediction information of other road users, the method further comprises:

[0023] predicting the behavior of other road users based on the visual data and / or radar data collected by the vehicle to obtain the behavior intention prediction information of other road users;

[0024] determining the behavior intention prediction information of the vehicle based on the control logic information and the vehicle body state information of the vehicle.

[0025] Optionally, in an embodiment of the present application, the determination of the behavior intention prediction information of the vehicle based on the control logic information and the vehicle body state information of the vehicle comprises:

[0026] determining a multi-dimensional driving impact factor that determines the control logic information and the vehicle body state information of the vehicle;

[0027] assigning corresponding weight parameters to different dimensions of the driving impact factor;

[0028] obtaining the behavior intention prediction information of the vehicle based on the parameter values of the multi-dimensional driving impact factor of the vehicle at present and the weight parameters.

[0029] Optionally, in an embodiment of the present application, the method further comprises: when the quality evaluation information is higher than or equal to the preset quality, controlling and adjusting the vehicle headlamp according to the predicted driving trajectory.

[0030] In a second aspect, based on the control method of the vehicle headlamp in the first aspect of the present application, an embodiment of the present application further provides a control device of a vehicle headlamp, comprising:

[0031] a planning module configured to obtain a predicted driving trajectory of the vehicle based on inertial navigation data and driving map information of the vehicle;

[0032] an assignment module, configured to assign a corresponding quality influence weight ratio parameter to different visual feature parameters contained in the visual data collected by the host vehicle based on the predicted driving track of the host vehicle, the behavior intention prediction information of the host vehicle, and the behavior intention prediction information of other road users;

[0033] a calculation module, configured to determine quality evaluation information of the visual data collected by the host vehicle based on the quality influence weight ratio parameter;

[0034] a control module, configured to perform control adjustment on the vehicle headlamp according to the quality evaluation information when it is determined that the quality evaluation information is lower than a preset quality, the control adjustment at least including one of light-dark degree adjustment, low beam or high beam adjustment, and irradiation field angle adjustment.

[0035] In a third aspect, an embodiment of the present application further provides a computer storage medium, which has computer executable instructions stored thereon, and the computer executable instructions are executed to perform any one of the vehicle headlamp control methods according to the first aspect of the present application.

[0036] In a fourth aspect, an embodiment of the present application further provides an electronic device, which includes a processor, a memory, a communication interface, and a communication bus, and the processor, the memory, and the communication interface complete communication with each other through the communication bus.

[0037] The memory is used to store at least one executable instruction, and the executable instruction causes the processor to perform any one of the vehicle headlamp control methods according to the first aspect of the present application.

[0038] The present application provides a vehicle headlamp control method, device, and related equipment. According to inertial navigation data and driving map information of a host vehicle, a predicted driving track of the host vehicle is obtained. Based on the predicted driving track of the host vehicle, behavior intention prediction information of the host vehicle, and behavior intention prediction information of other road users, a corresponding quality influence weight ratio parameter is assigned to different visual feature parameters contained in the visual data collected by the host vehicle. Based on the quality influence weight ratio parameter, quality evaluation information of the visual data collected by the host vehicle is determined. When it is determined that the quality evaluation information is lower than a preset quality, control adjustment is performed on the vehicle headlamp according to the quality evaluation information, and the control adjustment at least includes one of light-dark degree adjustment, low beam or high beam adjustment, and irradiation field angle adjustment. Through the above vehicle headlamp control method, a suitable irradiation area control can be provided according to the driving environment, to meet the high standard requirements of the perception data of the vehicle in the driving environment, to provide better driving assistance for the intelligent driving vehicle, and to improve the driving safety and user driving experience of the intelligent driving vehicle. BRIEF DESCRIPTION OF DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the accompanying drawings needed to be used in the embodiments or prior art description will be briefly introduced as follows. Obviously, the accompanying drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art based on these drawings.

[0040] Figure 1 A working flow diagram of a control method of a vehicle headlamp provided by the embodiments of the present application;

[0041] Figure 2 A structural diagram of a control device of a vehicle headlamp provided by the embodiments of the present application.

[0042] Figure 3 A structural diagram of an electronic device provided by the embodiments of the present application. DETAILED DESCRIPTION

[0043] In order for those skilled in the art to better understand the technical solutions in the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely in conjunction with the accompanying drawings of the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art should belong to the scope of protection of the present application.

[0044] It should be understood that each step described in the method embodiments of the present application can be executed in different order and / or in parallel. In addition, the method embodiments can include additional steps and / or omit the execution of the steps shown. The scope of the present application is not limited in this respect.

[0045] Embodiment one,

[0046] The embodiments of the present application provide a control method of a vehicle headlamp, as shown in Figure 1 , Figure 1 A working flow diagram of a control method of a vehicle headlamp provided by the embodiments of the present application, including:

[0047] In the embodiment of the present application, the inertial navigation data of the vehicle is collected in real time by an inertial navigation system (INS) carried by the vehicle, and is collected in real time by a vehicle-mounted IMU (Inertial Measurement Unit), generally includes one or more electronic units composed of a speedometer, a gyroscope or a magnetometer, and the collected data includes vehicle position information, speed information and vehicle attitude information, etc. The driving map information is obtained in real time by a vehicle positioning system, for example, high-precision map information. In the embodiment of the present application, the driving trajectory of the vehicle at the next time is predicted through the two dimensions of the inertial navigation data and the driving map information, so as to reduce the amount of data to be processed when the driving trajectory is predicted, and to ensure that the obtained prediction result has good accuracy. To meet the high timeliness demand of vehicle headlight control.

[0048] Specifically, in an optional implementation of the embodiment of the present application, the predicted driving trajectory of the vehicle is obtained according to the inertial navigation data and the driving map information of the vehicle, including: extracting the acceleration, angular velocity and speed information of the vehicle from the obtained inertial navigation data, and extracting the road topology structure, lane line information and traffic sign information of the driving road of the vehicle from the high-precision map information; performing Kalman filter processing on the extracted acceleration, angular velocity and speed information of the vehicle; converting the extracted road topology structure, lane line information and traffic sign information into a local map representation centered on the vehicle; using a preset interacting multiple model (IMM), combining multiple motion models, dynamically adjusting the weight of each motion model through a model switching probability, combining the filtered acceleration, angular velocity and speed information, predicting the driving position and speed of the vehicle at the next stage; using the local map representation to optimize the predicted driving position and speed of the vehicle at the next stage, so that the predicted driving trajectory conforms to the topology structure of the current road, to obtain the predicted driving trajectory of the vehicle, wherein the multiple motion models include at least one or more of constant speed model, uniform acceleration model and other motion neural network models. In this way, the noise and abnormal values in the collected inertial navigation data are eliminated, and high-precision and fast vehicle trajectory prediction can be realized under the condition of only needing to process less data, so as to better meet the working scene demand of timely controlling the vehicle headlight in the embodiment.

[0049] Step S102, based on the predicted driving trajectory of the vehicle, the behavior intention prediction information of the vehicle and the behavior intention prediction information of other road users, assign corresponding quality impact weight ratio parameters to different visual feature parameters contained in the visual data collected by the vehicle. In the embodiment of the present application, the predicted driving trajectory is used to represent the possible movement route of the vehicle in the next stage based on the current control logic. The behavior intention prediction information of the vehicle is used to represent the possible driving state of the vehicle in the next stage, such as acceleration, brake or turn signal information. The behavior intention prediction information of other road users is used to represent the change information of other people, vehicles, road signs or other road obstacles in the current road relative to the position of the vehicle in the next stage. Based on the three dimensions, the present application assigns quality impact weight ratio parameters to different visual feature parameters in the visual data of the vehicle. For example, when the visual data is a single image data, the visual feature parameters can be the pixel value, brightness and contrast information of the image data, and corresponding quality impact weight ratio parameters are assigned to these visual feature parameters. When the visual data is video data, key image frames need to be extracted from the video data as target visual data for assigning corresponding quality impact weight ratio parameters. The present application embodiment illustrates an example here: when the vehicle is driving, the predicted driving trajectory indicates that the vehicle needs to go straight through a crossroads with a traffic light, the behavior intention prediction information of the vehicle indicates that the vehicle will continue to go straight, and the prediction information of other road users indicates that the red light of the crossroads will turn green at the next moment. In order to identify whether there are pedestrians crossing the road at a distance, the vehicle needs to turn on the high beam or increase the brightness of the headlight as soon as possible. At this time, it is necessary to assign higher image quality impact weight ratio parameters to the brightness and contrast in the visual data through this step, and use these two high weight ratio parameters to affect the control and adjustment of the vehicle headlight, so that the vehicle can quickly obtain high-quality images of the crossroads and provide better driving assistance for the vehicle.

[0050] Step S103, based on the quality impact weight ratio parameters, determine the quality evaluation information of the visual data collected by the vehicle. The quality evaluation information is determined by weighted summation. The present application evaluates the quality of the current visual data in this way, and the evaluation result is reasonable and reliable, which provides adaptive adjustment basis for the control and adjustment of the vehicle headlight, and ensures that the visual data obtained by the vehicle in the next stage has high visual quality, so as to better meet the needs of vehicle driving.

[0051] In step S104, when it is determined that the quality evaluation information is lower than the preset quality, the vehicle headlamp is controlled and adjusted according to the quality evaluation information, and the control and adjustment at least includes one of the dimming adjustment, the low beam or high beam adjustment, and the illumination field angle adjustment. Through the control and adjustment in multiple dimensions, the embodiment of the present application can better meet the specific needs of the collected visual data of the vehicle at the next moment, improve the driving safety of the vehicle, and improve the user's driving experience.

[0052] Optionally, in an embodiment of the present application, the quality influence weight ratio parameters corresponding to different visual feature parameters of the visual data of the host vehicle are assigned based on the predicted driving trajectory of the host vehicle, the behavior intention prediction information of the host vehicle, and the behavior intention prediction information of other road users, including: the visual data is divided into grid regions, the visual feature parameters of the visual data corresponding to each grid region are determined, the visual feature parameters at least include one of the brightness, the contrast, and the definition of the visual image, the quality influence weight ratio parameters corresponding to different visual feature parameters of each grid region are assigned based on the predicted driving trajectory of the host vehicle, the behavior intention prediction information of the host vehicle, and the behavior intention prediction information of other road users, and the feature weighted average value of each grid region is calculated according to the quality influence weight ratio parameters, and the feature weighted average value is taken as the quality evaluation information of the visual data. In this stage, the embodiment of the present application divides the visual data into multiple grid regions, and then calculates the feature weighted average value of multiple feature parameters of each grid region, so that the quality of different regions of the visual data can be evaluated in more detail, so that it can be better determined that the visual data is not clear enough due to which region or caused by the low-quality reason of collecting due to the reflection lamp and other reasons, so that the vehicle headlamp can be better adjusted to meet the needs of the vehicle, so that the vehicle can obtain high-quality visual data that meets the needs of the vehicle in the next stage.

[0053] Optionally, in an embodiment of the present application, when it is determined that the quality evaluation information is lower than the preset quality, the control adjustment of the vehicle headlamp according to the quality evaluation information comprises: fixing the quality influence weight ratio parameter of the current driving stage; recalculating the quality evaluation information of the visual data collected by the vehicle after the control adjustment based on the fixed quality influence weight ratio parameter; and stopping the control adjustment of the vehicle headlamp according to the quality evaluation information when the recalculated quality evaluation information is greater than or equal to the preset quality. In this stage of the embodiment of the present application, the quality influence weight ratio parameter of each grid area is fixed, so it is not necessary to allocate a new quality influence weight ratio parameter for each grid area, which improves the efficiency of recalculating the quality of the collected visual data to a certain extent, realizes the continuous steady-state dynamic adjustment of the vehicle headlamp under the premise of meeting the current driving demand of the vehicle, and avoids excessively bright adjustment of the vehicle headlamp, thereby avoiding the influence of excessively high control adjustment on the quality of the collected visual data, such as overexposure or reflection in the image.

[0054] Optionally, in an embodiment of the present application, the grid area division of the visual data comprises: determining the illumination field of each headlamp of the vehicle, and dividing the visual data into grid areas from near to far based on the illumination field and the shooting area of the vehicle camera. In this embodiment of the present application, this limitation is made to avoid the possibility that the areas in the visual data which do not play a role in driving assistance are also divided and involved in the adjustment of the vehicle headlamp, and to reduce the number of grid areas that need to be concerned in the data processing process, so that the influence of the effective area on the quality of the visual data is more obvious, and the amount of data to be processed when evaluating the quality of the visual data is reduced, thereby improving the accuracy and efficiency of the corresponding control adjustment of the vehicle headlamp in different dimensions to a certain extent.

[0055] Optionally, in an embodiment of the present application, when the corresponding quality influence weight ratio parameter is allocated to each grid area according to different visual feature parameters, the quality influence weight ratio allocated to the grid area close to the front headlamp of the vehicle body is greater than the quality influence weight ratio allocated to the grid area far from the front headlamp of the vehicle body, and the quality influence weight ratio allocated to the grid area in which the illumination fields of multiple front headlamps overlap is greater than the quality influence weight ratio allocated to the grid area which is not overlapped. In this step of the embodiment of the present application, the allocated quality influence weight ratio parameter is limited as described above, which improves the scientific rationality of the allocated quality influence weight ratio.

[0056] Optionally, in an embodiment of the present application, the different visual feature parameters contained in the visual data of the host vehicle are assigned corresponding quality influence weight ratio parameters based on the driving track of the host vehicle, the behavior intention prediction information of the host vehicle and the behavior intention prediction information of other road users, and before that, the method further comprises: predicting the behavior of other road users based on the visual data and / or radar data collected by the host vehicle to obtain the behavior intention prediction information of other road users; and determining the behavior intention prediction information of the host vehicle based on the control logic information and the vehicle body state information of the host vehicle. In this way, high-precision behavior intention prediction information can be obtained while ensuring that less data needs to be processed.

[0057] Optionally, in an embodiment of the present application, the determination of the behavior intention prediction information of the host vehicle based on the control logic information and the vehicle body state information of the host vehicle comprises: determining multi-dimensional driving influence factors that determine the control logic information and the vehicle body state information of the host vehicle; assigning corresponding weight parameters to different dimensions of driving influence factors; and obtaining the behavior intention prediction information of the host vehicle based on the parameter values of the multi-dimensional driving influence factors of the host vehicle at present and in combination with the weight parameters. In this embodiment, an example is listed for illustration: the acceleration, speed, steering angle, vehicle driving inclination angle and vehicle navigation information of the vehicle are determined as multi-dimensional driving influence factors, and different weight parameters are set for these driving influence factors under different dimensions of driving control logic, so as to facilitate the prediction of the behavior intention prediction information of the host vehicle at the next stage based on the determined parameter values of the driving influence factors. For example, the weight parameters of the speed / acceleration and other driving influence factors are set to be higher under the straight-line driving control logic, and the weight parameters of the steering angle and the vehicle driving inclination angle and other driving influence factors are set to be higher under the steering or parking control logic, so that the driving intention prediction information of the host vehicle obtained according to the parameter values of the determined driving influence factors and the corresponding weight parameters is more accurate and reliable.

[0058] Optionally, in an embodiment of the present application, the method further comprises: when the quality evaluation information is higher than or equal to the preset quality, controlling and adjusting the vehicle headlamp according to the predicted driving track. In this embodiment, when the quality evaluation information is determined to be higher than the preset quality, it indicates that the current visual data can meet the driving requirements of the host vehicle at the current stage, and no additional factors are needed to control and adjust the vehicle headlamp. Only the headlamp that meets the requirements of the corresponding driving position needs to be controlled according to the predicted driving track information. In this way, the complexity of the headlamp control and adjustment in this embodiment and the occupation of the vehicle data processing resources in the control and adjustment process are reduced.

[0059] The application provides a control method of a vehicle headlamp, the method comprises the following steps: acquiring a predicted driving track of a host vehicle according to inertial navigation data and driving map information of the host vehicle; assigning a corresponding quality influence weight ratio parameter to different visual feature parameters contained in visual data collected by the host vehicle based on the predicted driving track of the host vehicle, behavior intention prediction information of the host vehicle and behavior intention prediction information of other road users; determining quality evaluation information of the visual data collected by the host vehicle based on the quality influence weight ratio parameter; and controlling and adjusting the vehicle headlamp according to the quality evaluation information when it is determined that the quality evaluation information is lower than a preset quality, wherein the control and adjustment at least comprises one of light and dark degree adjustment, low beam or high beam adjustment and irradiation field angle adjustment. Through the above control method of the vehicle headlamp, a suitable irradiation area control can be provided according to the driving environment, so that the high standard requirement of the perception data of the vehicle in the intelligent driving is met, better driving assistance is provided for the intelligent driving vehicle, and the driving safety of the intelligent driving vehicle and the driving experience of the user are improved.

[0060] Embodiment two,

[0061] Based on the control method of the vehicle headlamp provided in the first embodiment of the application, the application further provides a control device of a vehicle headlamp, as shown in Figure 2 The control device of the vehicle headlamp provided in the application comprises: Figure 2 A planning module 201 is configured to acquire a predicted driving track of a host vehicle according to inertial navigation data and driving map information of the host vehicle.

[0062] The planning module 201 is configured to acquire a predicted driving track of a host vehicle according to inertial navigation data and driving map information of the host vehicle.

[0063] An assigning module 202 is configured to assign a corresponding quality influence weight ratio parameter to different visual feature parameters contained in visual data collected by the host vehicle based on the predicted driving track of the host vehicle, behavior intention prediction information of the host vehicle and behavior intention prediction information of other road users.

[0064] A calculating module 203 is configured to determine quality evaluation information of the visual data collected by the host vehicle based on the quality influence weight ratio parameter.

[0065] A control module 204 is configured to control and adjust the vehicle headlamp according to the quality evaluation information when it is determined that the quality evaluation information is lower than a preset quality, wherein the control and adjustment at least comprises one of light and dark degree adjustment, low beam or high beam adjustment and irradiation field angle adjustment.

[0066] Optionally, in an embodiment of the application, the assigning module 202 is further configured to:

[0067] grid region division is performed on the visual data;

[0068] a visual feature parameter of the visual data corresponding to each grid region is determined, the visual feature parameter at least including one feature parameter value of brightness, contrast and definition of the visual image;

[0069] a corresponding quality influence weight ratio parameter is assigned to each grid region for different visual feature parameters based on the predicted driving track of the vehicle, the behavior intention prediction information of the vehicle and the behavior intention prediction information of other road users;

[0070] a feature weighted average value of each grid region is calculated according to the quality influence weight ratio parameter, and the feature weighted average value is taken as the quality evaluation information of the visual data.

[0071] Optionally, in an embodiment of the present application, the control module 204 is further configured to:

[0072] the quality influence weight ratio parameter of the current driving stage is fixed;

[0073] the quality evaluation information of the visual data collected by the vehicle after control adjustment is recalculated based on the fixed quality influence weight ratio parameter;

[0074] when the recalculated quality evaluation information is greater than or equal to the preset quality, the control adjustment of the vehicle headlamp according to the quality evaluation information is stopped.

[0075] Optionally, in an embodiment of the present application, the assignment module 202 is further configured to:

[0076] the irradiation field of each headlamp of the vehicle is determined;

[0077] the grid region division from near to far is performed on the visual data based on the irradiation field and the camera shooting area.

[0078] Optionally, in an embodiment of the present application, the assignment module 202 is further configured to, when assigning the corresponding quality influence weight ratio parameter to each grid region for different visual feature parameters, set the quality influence weight ratio assigned to the grid region close to the front headlamp of the vehicle greater than the quality influence weight ratio assigned to the grid region far from the front headlamp of the vehicle, and the quality influence weight ratio assigned to the grid region where the irradiation fields of multiple front headlamps overlap is greater than the quality influence weight ratio assigned to the grid region where the irradiation fields do not overlap.

[0079] Optionally, in an embodiment of the present application, the device 20 further comprises a preprocessing module (not shown in the figure) for predicting the behavior of the other road users based on the visual data and / or radar data collected by the host vehicle before assigning the corresponding quality impact weight ratio parameters to the different visual feature parameters contained in the visual data of the host vehicle based on the driving trajectory of the host vehicle, the behavior intention prediction information of the host vehicle and the behavior intention prediction information of the other road users, and obtaining the behavior intention prediction information of the other road users; determining the behavior intention prediction information of the host vehicle based on the control logic information and the body state information of the host vehicle.

[0080] Optionally, in an embodiment of the present application, the preprocessing module is further configured to determine the multi-dimensional driving impact factors that determine the control logic information and the body state information of the host vehicle; assign the corresponding weight parameters to the driving impact factors of different dimensions; and obtain the behavior intention prediction information of the host vehicle based on the parameter values of the multi-dimensional driving impact factors of the host vehicle at present and in combination with the weight parameters.

[0081] Optionally, in an embodiment of the present application, the control module 204 is further configured to control and adjust the vehicle headlamp according to the predicted driving trajectory when the quality evaluation information is higher than or equal to the preset quality.

[0082] The present application provides a control device for a vehicle headlamp, which comprises a planning module configured to obtain a predicted driving trajectory of the host vehicle based on inertial navigation data and driving map information of the host vehicle; an assignment module configured to assign corresponding quality impact weight ratio parameters to different visual feature parameters contained in the visual data collected by the host vehicle based on the predicted driving trajectory of the host vehicle, the behavior intention prediction information of the host vehicle and the behavior intention prediction information of the other road users; a calculation module configured to determine quality evaluation information of the visual data collected by the host vehicle based on the quality impact weight ratio parameters; and a control module configured to control and adjust the vehicle headlamp according to the quality evaluation information when the quality evaluation information is lower than a preset quality, wherein the control and adjustment at least includes one of the following: adjusting the light-dark degree of the vehicle headlamp, adjusting the low beam or high beam of the vehicle headlamp, and adjusting the illumination field angle of the vehicle headlamp. The device of the embodiment of the present application has a simple structure and is easy to implement. Through the step-by-step processing between the various modules, the control mode of the vehicle headlamp can be more suitable for the current driving needs of the host vehicle, meet the business needs of obtaining high-standard road conditions and environment for the intelligent driving vehicle, and thus provide better driving assistance for the intelligent driving vehicle and improve the driving safety and user driving experience of the intelligent driving vehicle.

[0083] Embodiment three,

[0084] The embodiment of the present application further provides a storage medium, which has a computer program stored thereon, and the program is executed by a processor to implement any one of the control methods of the vehicle headlamp according to the embodiment one of the present application.

[0085] Embodiment four,

[0086] The embodiment of the present application further provides an electronic device, such as Figure 3 as shown in the figure, Figure 3 A structural schematic diagram of an electronic device 30 is provided for the embodiment of the present application, and the electronic device 30 comprises:

[0087] one or more processors 301, a communication interface 302, a memory 303 and a communication bus 304, the processor 301, the memory 303 and the communication interface 302 complete communication with each other through the communication bus 304;

[0088] The memory 303 is used for storing one or more programs.

[0089] When the one or more programs are executed by the one or more processors 301, the one or more processors 301 implement any one of the control methods of the vehicle headlamp according to the embodiment one of the present application.

[0090] So far, the specific embodiments of the present subject matter have been described in the present application. In some cases, the actions recorded in the claims can be executed in different orders and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing can be advantageous.

[0091] In the 1990s, it was quite obvious to distinguish whether an improvement in a technology was in hardware (e.g., improvement in circuit structures of diodes, transistors, switches, etc.) or in software (improvement in method flow). However, as technology has evolved, many improvements in method flow today can be considered as direct improvements in hardware circuit structures. Designers almost always obtain the corresponding hardware circuit structures by programming the improved method flow into hardware circuits. Therefore, it cannot be said that an improvement in a method flow cannot be implemented by hardware entity modules. For example, a programmable logic device (PLD) such as a field programmable gate array (FPGA) is an integrated circuit whose logic function is determined by user programming of the device. A designer programs a digital system layer "integrated" on a PLD by himself / herself, without having to ask a chip manufacturer to design and manufacture a special integrated circuit chip. Moreover, instead of manually manufacturing an integrated circuit chip, this programming is now mostly implemented by "logic compiler" software, which is similar to a software compiler used when developing a program, and the original code before compilation also has to be written in a specific programming language, which is called a hardware description language (HDL), and there are many types of HDLs, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc., and the most commonly used are VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should be aware that, as long as the method flow is logically programmed in the above-mentioned hardware description languages and programmed into an integrated circuit, a hardware circuit implementing the logical method flow can be easily obtained.

[0092] The controller can be implemented in any suitable way, for example, the controller can take the form of a microprocessor or processor and a computer readable medium storing computer readable program code, such as software or firmware, executable by the (micro)processor, logic gates, switches, an application specific integrated circuit (ASIC), a programmable logic controller and an embedded microcontroller, examples of which include but are not limited to the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20 and Silicone Labs C8051F320, the memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also know that, in addition to implementing the controller purely in computer readable program code, it is possible to implement the same functionality with logic gates, switches, an application specific integrated circuit, a programmable logic controller and an embedded microcontroller, etc. by logically programming the method steps. Such a controller can therefore be considered to be a hardware component, and the means included therein for implementing the various functions can also be considered to be structures within the hardware component. Alternatively, or even additionally, the means for implementing the various functions can be considered to be both a software module implementing the method and a structure within a hardware component.

[0093] The system layers, devices, modules or units illustrated by the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer can be a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0094] For the sake of description, the above devices are described in various units by functions respectively. Of course, the functions of each unit can be implemented in one or more software and / or hardware in the implementation of the present application.

[0095] It should also be noted that the terms "comprising", "including", or any other variant thereof are intended to cover non-exclusive inclusions, such that processes, methods, articles or devices including a series of elements not only include those elements, but also include other elements not explicitly listed, or other elements inherent to such processes, methods, articles or devices. Without more limitations, the element defined by the statement "including a" does not exclude the presence of another identical element in the process, method, article or device including the element.

[0096] Those skilled in the art will appreciate that embodiments of the present application can be readily used as a method, a system, or a computer program product. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer readable program code.

[0097] The present application can be described in the general context of computer- executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The present application can also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules can be located in both local and remote computer storage media including memory storage devices.

[0098] The various embodiments in the specification are described in progressive manner, and the same or similar parts between embodiments can be referred to each other, and each embodiment focuses on the difference from other embodiments. In particular, for system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant part can be referred to the part of the method embodiment.

[0099] The above merely provides embodiments of the present application, but does not serve to limit the present application. For those skilled in the art, various modifications and variations can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the scope of claims of the present application.

Claims

1. A method for controlling vehicle headlights, characterized in that, include: Based on the vehicle's inertial navigation data and driving map information, the predicted driving trajectory of the vehicle is obtained; Based on the predicted driving trajectory of the vehicle, the predicted behavioral intentions of the vehicle, and the predicted behavioral intentions of other road users, corresponding quality influence weight ratio parameters are assigned to different visual feature parameters contained in the visual data collected by the vehicle. Based on the aforementioned quality impact weight ratio parameter, the quality assessment information of the visual data collected by this vehicle is determined; When the quality assessment information is determined to be lower than the preset quality, the vehicle headlights are controlled and adjusted according to the quality assessment information. The control and adjustment include at least one of the following: adjusting the brightness of the vehicle headlights, adjusting the low beam or high beam, and adjusting the illumination field angle.

2. The vehicle headlight control method according to claim 1, characterized in that, The method of assigning corresponding quality influence weight ratio parameters to different visual feature parameters contained in the vehicle's visual data based on the vehicle's predicted driving trajectory, the vehicle's predicted behavioral intention information, and the predicted behavioral intention information of other road users includes: The visual data is divided into grid regions; Determine the visual feature parameters of the visual data corresponding to each grid region, wherein the visual feature parameters include at least one of the following: brightness, contrast, and sharpness of the visual image. Based on the predicted driving trajectory of the vehicle, the predicted behavioral intention of the vehicle, and the predicted behavioral intention of other road users, a corresponding quality influence weight ratio parameter is assigned to different visual feature parameters of each grid area. Based on the quality influence weight ratio parameter, the feature weighted average value of each grid region is calculated, and the feature weighted average value is used as the quality assessment information of the visual data.

3. The vehicle headlight control method according to claim 2, characterized in that, When it is determined that the quality assessment information is lower than a preset quality, the headlights of the vehicle are controlled and adjusted according to the quality assessment information, including: The weighting ratio parameter of the quality impact during the current driving phase is fixed; Based on a fixed quality influence weight ratio parameter, the quality assessment information of the visual data collected by the vehicle after control and adjustment is recalculated. When the recalculated quality assessment information is greater than or equal to the preset quality, the control and adjustment of the vehicle headlights based on the quality assessment information shall be stopped.

4. The vehicle headlight control method according to claim 2, characterized in that, The step of dividing the visual data into grid regions includes: Determine the illumination field of each headlight of the vehicle; Based on the illumination field and the shooting area of ​​the vehicle-mounted camera, the visual data is divided into grid regions from near to far.

5. The vehicle headlight control method according to claim 4, characterized in that, When assigning corresponding quality influence weight ratio parameters to different visual feature parameters of each grid region, the quality influence weight ratio assigned to grid regions closer to the headlights is set to be greater than that assigned to grid regions farther from the headlights. Furthermore, the quality influence weight ratio assigned to grid regions where the illumination fields of multiple headlights are superimposed is greater than that assigned to non-superimposed grid regions.

6. The vehicle headlight control method according to claim 1, characterized in that, Before assigning corresponding quality influence weight ratio parameters to different visual feature parameters contained in the vehicle's visual data based on the vehicle's driving trajectory, the vehicle's behavioral intention prediction information, and the behavioral intention prediction information of other road users, the method further includes: Based on the visual data and / or radar data collected by this vehicle, the behavior of other road users is predicted, and information on the behavioral intentions of other road users is obtained. Based on the vehicle's control logic information and vehicle status information, the vehicle's behavioral intention prediction information is determined.

7. The vehicle headlight control method according to claim 6, characterized in that, The determination of the vehicle's behavioral intention prediction information based on the vehicle's control logic information and vehicle status information includes: Identify the multi-dimensional driving influencing factors that determine the vehicle's control logic information and body status information; Assign corresponding weight parameters to driving influencing factors of different dimensions; Based on the parameter values ​​of the current multi-dimensional driving influencing factors of the vehicle, and combined with the weight parameters, the behavioral intention prediction information of the vehicle is obtained.

8. The vehicle headlight control method according to claim 1, characterized in that, The method further includes: when the quality assessment information is higher than or equal to the preset quality, controlling and adjusting the vehicle headlights according to the predicted driving trajectory.

9. A control device for vehicle headlights, characterized in that, include: The planning module is used to obtain the predicted driving trajectory of the vehicle based on the vehicle's inertial navigation data and driving map information; The allocation module is used to assign corresponding quality influence weight ratio parameters to different visual feature parameters contained in the visual data collected by the vehicle, based on the predicted driving trajectory of the vehicle, the predicted behavioral intention information of the vehicle, and the predicted behavioral intention information of other road users. The calculation module is used to determine the quality assessment information of the visual data collected by the vehicle based on the quality influence weight ratio parameter. The control module is used to control and adjust the vehicle headlights according to the quality assessment information when it is determined that the quality assessment information is lower than the preset quality. The control and adjustment includes at least one of adjusting the brightness of the vehicle headlights, adjusting the low beam or high beam, and adjusting the illumination field angle.

10. A computer storage medium, characterized in that, The computer storage medium stores computer-executable instructions, which, when executed, perform the vehicle headlight control method as described in any one of claims 1-8.

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