Interface optimization method, electronic device, and vehicle

Abstract

This application relates to the field of vehicle technology, providing an interface optimization method, an electronic device, and a vehicle. The method includes: displaying a scene interface corresponding to a perceived object identified by the vehicle; calculating the clutter level of the scene interface based on visual elements in the scene interface; adjusting the display parameters of the visual elements based on the clutter level of the scene interface; and displaying the adjusted scene interface. This method can improve the orderliness of visual elements in the adjusted scene interface, thereby improving the visual effect of the adjusted scene interface.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, specifically to an interface optimization method, electronic devices, and vehicles. Background Technology

[0002] In vehicle driver assistance systems, the perceived information from the vehicle is typically visualized on an in-vehicle display screen. Related technologies usually perform basic preprocessing on the perceived information, such as de-jittering, filtering, and time synchronization, before directly outputting the display. However, this approach can easily lead to a cluttered interface layout and fails to meet the visualization requirements of driver assistance systems. Summary of the Invention

[0003] This application provides an interface optimization method, electronic device, and vehicle to solve the technical problem that the layout of the display interface is chaotic and cannot meet the visualization display requirements of advanced driver assistance.

[0004] The first aspect of this application provides an interface optimization method, the method comprising: displaying a scene interface corresponding to a perceived object identified by a vehicle; calculating the clutter level of the scene interface based on visual elements in the scene interface; adjusting the display parameters of the visual elements based on the clutter level of the scene interface; and displaying the adjusted scene interface.

[0005] This application embodiment uses the perceived object identified by the vehicle to display the scene interface of the perceived object accordingly. Based on the visual elements in the scene interface, the clutter level of the scene interface can be quantified. Then, based on the clutter level of the scene interface, the display parameters of the visual elements are adjusted accordingly to improve the orderliness of the visual elements in the adjusted scene interface, thereby improving the user experience.

[0006] According to an embodiment of this application, the sensing objects include sensing objects identified by the vehicle at multiple times. The method further includes: preprocessing the sensing objects, the preprocessing including: filtering multiple sensing objects based on the number of times each sensing object appears at the multiple times; determining a first object set based on the filtered sensing objects; performing de-overlap processing on the sensing objects in the first object set based on the position overlap rate of any two sensing objects in the first object set to obtain a second object set; and calibrating the sensing objects with lower confidence using the sensing objects with higher confidence for any two sensing objects in the second object set to obtain preprocessed sensing objects.

[0007] This application embodiment filters multiple sensing objects by the number of times each sensing object appears at the multiple times, which can effectively reduce the flickering phenomenon of visual elements corresponding to sensing objects in the scene interface, thereby improving the stability and smoothness of the scene interface; by performing de-overlap processing on the sensing objects in the first object set, abnormal sensing objects in the second object set can be avoided, thereby improving the accuracy of the second object set; by using sensing objects with higher confidence to calibrate sensing objects with lower confidence, the accuracy and reliability of the preprocessed sensing objects can be further improved.

[0008] According to an embodiment of this application, the sensing object includes a landmark object, and the step of displaying a scene interface corresponding to the sensing object based on the vehicle-identified sensing object includes: obtaining an interface template matching the landmark object; filling the object area of ​​the interface template with the visual elements corresponding to the sensing object to obtain the scene interface.

[0009] In this embodiment, by using the iconic objects in the perceived objects, a corresponding interface template can be matched. By filling the object area of ​​the interface template with the visual elements corresponding to the perceived objects, the efficiency of obtaining the scene interface can be improved.

[0010] According to an embodiment of this application, the step of calculating the disorder of the scene interface based on the visual elements in the scene interface includes: calculating the disorder of the visual elements based on the area ratio, spatial disorder, and matching degree between the visual elements and the scene interface; and calculating the disorder of the scene interface based on the display position of the visual elements in the scene interface and the disorder of the visual elements.

[0011] This application embodiment can quantify the disorder of a visual element by measuring its area ratio, spatial disorder, and matching degree between the visual element and the scene interface. Based on the display position of the visual element on the scene interface and the disorder of the visual element, the disorder of the scene interface can be reasonably determined.

[0012] According to an embodiment of this application, the visual elements include visual elements corresponding to dynamic obstacles in the perceived object. Adjusting the display parameters of the visual elements based on the clutter level of the scene interface includes: updating the visual elements corresponding to the dynamic obstacle based on the object identifier of the dynamic obstacle when the clutter level of the scene interface is greater than a first threshold; adjusting the transparency of the visual elements corresponding to the dynamic obstacle when the clutter level of the scene interface is less than or equal to the first threshold and greater than or equal to a second threshold, wherein the first threshold is greater than the second threshold; and not adjusting the visual elements corresponding to the dynamic obstacle when the clutter level of the scene interface is less than the second threshold.

[0013] In this embodiment of the application, when the clutter level of the scene interface is greater than a first threshold, the visual elements corresponding to the dynamic obstacles are updated according to the object identifiers of the dynamic obstacles, which can improve the orderliness of the visual elements corresponding to the dynamic obstacles. When the clutter level of the scene interface is less than or equal to the first threshold and greater than or equal to the second threshold, the transparency of the visual elements corresponding to the dynamic obstacles is adjusted, which can not only improve the aesthetics of the scene interface, but also broaden the driver's field of vision.

[0014] According to an embodiment of this application, the visual elements include visual elements corresponding to a first static obstacle in the sensing object and / or visual elements corresponding to a second static obstacle in the sensing object, wherein the second static obstacle is used to indicate the scene in which the vehicle is located. The method further includes: performing perspective processing on the visual elements corresponding to the first static obstacle; and / or enhancing at least one of the brightness, hue, sharpness, and saturation of the visual elements corresponding to the second static obstacle.

[0015] This application embodiment improves the aesthetics of the scene interface by performing perspective processing on the visual elements corresponding to the first static obstacle. By enhancing at least one of the brightness, hue, sharpness, and saturation of the visual elements corresponding to the second static obstacle, the visual cueing effect of the visual elements corresponding to the second static obstacle can be improved, thus optimizing the driving experience.

[0016] According to an embodiment of this application, after adjusting the display parameters of the visual elements, the method further includes: performing illumination consistency processing on the visual elements in the adjusted scene interface based on the environmental information of the vehicle's environment; and / or performing color consistency adjustment on the visual elements in the adjusted scene interface based on the environmental image of the vehicle's environment; and / or generating a gradient mask based on the mask features of the adjusted scene interface, and rendering the adjusted scene interface according to the gradient mask.

[0017] This application embodiment improves the realism and naturalness of the adjusted scene interface by applying lighting consistency processing to the visual elements in the adjusted scene interface; it also makes the colors of the adjusted scene interface more harmonious by adjusting the color consistency of the visual elements in the adjusted scene interface, thereby enhancing the overall display effect of the adjusted scene interface; and it effectively eliminates the abruptness of the adjusted scene interface boundaries by rendering the adjusted scene interface through the gradient mask, thereby improving the visual effect.

[0018] According to an embodiment of this application, the method further includes: determining the user's travel scenario based on the vehicle's driving information and the user's behavioral characteristics; and adjusting the background color of the adjusted scenario interface based on the travel scenario.

[0019] This application embodiment can accurately determine the user's travel scenario by combining the vehicle's driving information and the user's behavioral characteristics. Then, based on the travel scenario, the background color of the adjusted scenario interface can be reasonably adjusted to improve the matching degree between the scenario interface and the user's emotions.

[0020] A second aspect of this application provides an interface optimization device, the device comprising: a display unit for displaying a scene interface corresponding to a perceived object identified by a vehicle; a calculation unit for calculating the clutter level of the scene interface based on visual elements in the scene interface; an optimization unit for adjusting the display parameters of the visual elements based on the clutter level of the scene interface; the display unit is further configured to display the adjusted scene interface.

[0021] A third aspect of this application provides an electronic device, the electronic device comprising: a memory for storing computer programs; and a processor for executing the computer programs stored in the memory to implement the interface optimization method.

[0022] A fourth aspect of this application provides a vehicle equipped with an electronic device for executing the interface optimization method.

[0023] A fifth aspect of this application provides a computer-readable storage medium storing a computer program, which is executed by a processor in an electronic device to implement the interface optimization method. Attached Figure Description

[0024] Figure 1 This is an application scenario diagram of the interface optimization method provided in one embodiment of this application.

[0025] Figure 2 This is a flowchart of an interface optimization method provided in an embodiment of this application.

[0026] Figure 3 This is a detailed flowchart of the preprocessing of the perceived object provided in one embodiment of this application.

[0027] Figure 4 This is a schematic diagram of a scene interface provided in an embodiment of this application.

[0028] Figure 5 This is a schematic diagram showing the relationship between the level of disorder and the processing method of a scene interface provided in an embodiment of this application.

[0029] Figure 6 This is a flowchart of an interface optimization method provided in another embodiment of this application.

[0030] Figure 7 This is a schematic diagram of the framework of an interface optimization method provided in an embodiment of this application.

[0031] Figure 8 This is a schematic diagram of the framework of a method for adjusting the display parameters of a visual element provided in an embodiment of this application.

[0032] Figure 9 This is a schematic diagram of the framework of a method for adjusting the display parameters of the visual elements provided in another embodiment of this application.

[0033] Figure 10 This is a functional block diagram of an interface optimization device provided in an embodiment of this application.

[0034] Figure 11 This is a schematic diagram of the structure of an electronic device that implements the interface optimization method according to an embodiment of this application. Detailed Implementation

[0035] To make the objectives, technical solutions, and advantages of this application clearer, the application will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0036] It should be noted that in this application, "at least one" means one or more, and "more than one" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and drawings of this application are used to distinguish similar objects, not to describe a specific order or sequence.

[0037] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a specific manner. Unless otherwise specified, the following embodiments and features described herein can be combined with each other.

[0038] In vehicle driver assistance systems, the perceived information from the vehicle is typically visualized on an in-vehicle display screen. Related technologies usually perform basic preprocessing such as de-jittering, filtering, and time synchronization on the perceived information before directly outputting it for display. However, when the vehicle identifies a large number of objects, this approach can easily lead to a cluttered interface layout and fail to meet the visualization requirements of driver assistance systems.

[0039] Based on the above problems, this application provides an interface optimization method. By recognizing the perceived object from the vehicle, the scene interface of the perceived object can be displayed accordingly. Then, based on the clutter of the scene interface, the display parameters of the visual elements are adjusted accordingly to improve the orderliness of the visual elements in the adjusted scene interface, thereby improving the user experience.

[0040] like Figure 1 The diagram shown is an application scenario diagram of the interface optimization method provided in an embodiment of this application.

[0041] In some embodiments of this application, the interface optimization method can be applied to the electronic device 100. The electronic device 100 can be installed in a vehicle 10; for example, the electronic device 100 can be an in-vehicle terminal in the vehicle 10. The vehicle 10 may also include a camera 101, a radar 102, and a steering wheel angle sensor 103.

[0042] In some embodiments of this application, the electronic device 100 can acquire multi-source perception information of the vehicle 10. For example, the electronic device 100 acquires environmental images of the vehicle's environment via a camera 101. The electronic device 100 can also acquire point cloud data of the vehicle's environment via a radar 102. The electronic device 100 can also acquire environmental information of the vehicle's environment via an environmental sensor 103, which may include, but is not limited to, illumination parameters, rain / fog levels, and object information of landmarks.

[0043] In other embodiments of this application, the electronic device 100 may also acquire vehicle driving information, such as the vehicle's departure location and departure time, through other sensors installed in the vehicle 10.

[0044] In other embodiments, the electronic device 100 may also be an electronic product that communicates with the vehicle 10. For example, the electronic device 100 may be a personal computer, tablet computer, smartphone, personal digital assistant (PDA), game console, interactive network television (IPTV), smart wearable device, etc.

[0045] Electronic device 100 may include network devices and / or user devices. Among them, network devices include, but are not limited to, a single network electronic device, a group of electronic devices consisting of multiple network electronic devices, or a cloud based on cloud computing consisting of a large number of hosts or network electronic devices.

[0046] The network where electronic device 100 is located may include, but is not limited to: the Internet, wide area network, metropolitan area network, local area network, and virtual private network (VPN).

[0047] like Figure 2 The diagram shown is a flowchart of an interface optimization method provided in one embodiment of this application. Depending on different requirements, the order of the steps in this flowchart can be changed, and some steps can be omitted.

[0048] S201, based on the perceived objects identified by the vehicle, displays the scene interface corresponding to the perceived objects.

[0049] In at least one embodiment of this application, an electronic device acquires multi-source sensor information about the vehicle's environment at multiple times. The multi-source sensor information at each time point may include, but is not limited to, visual images and point cloud data. The electronic device identifies the multi-source sensor information at each time point to obtain the perceived objects identified by the vehicle at each time point. The perceived objects may include the perceived objects identified by the vehicle at multiple times. The perceived objects may include, but are not limited to, dynamic obstacles, first static obstacles, and second static obstacles. Dynamic obstacles may represent obstacles in motion; for example, dynamic obstacles may include, but are not limited to, pedestrians and vehicles. Second static obstacles may represent obstacles in a stationary state that can be used to indicate the scene in which the vehicle is located. Second static obstacles may also be referred to as landmark objects; for example, second static obstacles include, but are not limited to, traffic signs and buildings. First static obstacles may include static obstacles other than second static obstacles; for example, first static obstacles include, vehicles in a stationary state and bicycles in a stationary state.

[0050] In at least one embodiment of this application, in order to improve the stability and accuracy of the sensed object, the electronic device can preprocess the sensed object. The method of preprocessing by the electronic device can be referred to... Figure 3 The process is shown below.

[0051] In at least one embodiment of this application, the sensing object includes a landmark object, which can represent a sensing object with a fixed location. The landmark object may include, but is not limited to, traffic signs and buildings.

[0052] In at least one embodiment of this application, the electronic device displays a scene interface corresponding to a perceived object based on a perceived object identified by the vehicle, including: obtaining an interface template matching a landmark object; and filling the object area of ​​the interface template with visual elements corresponding to the perceived object to obtain the scene interface.

[0053] In some embodiments of this application, the electronic device can store the correspondence between iconic objects and interface templates in a database. Based on this correspondence, the electronic device can retrieve an interface template matching the iconic object from the database. For example, the electronic device can retrieve the interface template "intersection scene template" corresponding to the iconic object "intersection".

[0054] In some embodiments of this application, the electronic device can fill the object area of ​​the interface template with the visual elements corresponding to the preprocessed perceived object. For example, the electronic device can fill the vehicle area of ​​the interface template with the visual elements corresponding to the perceived object "vehicle," and the visual elements corresponding to the perceived object "vehicle" can be vehicle models. As another example, the electronic device can fill the lane line area of ​​the interface template with the visual elements corresponding to the perceived object "lane line."

[0055] In this embodiment, by using the iconic objects in the perceived objects, the corresponding interface template can be matched. By filling the object area of ​​the interface template with the visual elements corresponding to the perceived objects, the efficiency of obtaining the scene interface can be improved.

[0056] S202, Calculate the clutter level of the scene interface based on the visual elements in the scene interface.

[0057] In at least one embodiment of this application, the electronic device calculates the disorder of the scene interface based on visual elements in the scene interface, including: calculating the disorder of visual elements based on the area ratio of visual elements, spatial disorder, and matching degree between visual elements and scene interface; and calculating the disorder of the scene interface based on the display position of visual elements in the scene interface and the disorder of visual elements.

[0058] In some embodiments of this application, the electronic device can calculate the area of ​​the circumscribed quadrilateral of the visual element corresponding to the preprocessed perceived object as the element area of ​​the visual element, and the electronic device can calculate the ratio of the element area of ​​each visual element to the area of ​​the scene interface as the area ratio of each visual element.

[0059] In some embodiments of this application, the electronic device connects any two visual elements and the intersection points in the statistical scene interface (e.g., Figure 4 The number of dots shown is taken as the number of intersections. For example... Figure 4 The scene interface shown has 4 intersection points.

[0060] In some embodiments of this application, the electronic device determines the spatial disorder of visual elements based on the number of intersection points. The spatial disorder of visual elements is proportional to the number of intersection points.

[0061] In some embodiments of this application, the electronic device encodes the object category of the perceived object corresponding to the visual element to obtain a first encoded feature, and encodes the scene type corresponding to the scene interface to obtain a second encoded feature. The electronic device calculates the similarity between the first encoded feature and the second encoded feature as the matching degree between the visual element and the scene interface. The matching degree between the visual element and the scene interface can be used to represent the probability that the perceived object corresponding to the visual element appears in the scene corresponding to the scene interface. For example, the matching degree between the visual element corresponding to the perceived object "vehicle" and the scene interface corresponding to the "intersection" scene is greater than the matching degree between the visual element corresponding to the perceived object "vehicle" and the scene interface corresponding to the "green belt" scene.

[0062] In some embodiments of this application, the electronic device performs a weighted sum operation on the area ratio, spatial disorder, and matching degree between visual elements and scene interface to obtain the disorder of visual elements.

[0063] In some embodiments of this application, the vehicle model corresponding to the vehicle is located at the center of the scene interface. The electronic device can determine the display position of the visual element on the scene interface based on the positional relationship between the perceived object corresponding to the visual element and the vehicle. Different display positions correspond to different position weights. For example, the position weight of the central area of ​​the scene interface is greater than the position weight of the edge area of ​​the scene interface. The electronic device can also determine the orientation of the vehicle model corresponding to the vehicle model in the scene interface based on the steering wheel orientation of the vehicle. The position weight corresponding to the orientation of the vehicle model in the scene interface is greater than the position weight corresponding to other orientations in the scene interface.

[0064] In some embodiments of this application, the electronic device performs a weighted sum calculation on the clutter of visual elements based on the position weights corresponding to the display positions of the visual elements, thereby obtaining the clutter level of the scene interface.

[0065] The embodiments of this application can quantify the disorder of visual elements by measuring their area ratio, spatial disorder, and matching degree between visual elements and scene interfaces. Based on the display position of visual elements on scene interfaces and the disorder of visual elements, the disorder of scene interfaces can be reasonably determined.

[0066] S203, adjust the display parameters of visual elements according to the clutter level of the scene interface.

[0067] In at least one embodiment of this application, the visual elements include visual elements corresponding to dynamic obstacles in the perceived object, which can also be referred to as temporary clutter. The electronic device adjusts the display parameters of the visual elements according to the clutter level of the scene interface, including: updating the visual elements corresponding to the dynamic obstacles based on the object identifier of the dynamic obstacles when the clutter level of the scene interface is greater than a first threshold; adjusting the transparency of the visual elements corresponding to the dynamic obstacles when the clutter level of the scene interface is less than or equal to the first threshold and greater than or equal to a second threshold; and not adjusting the visual elements corresponding to the dynamic obstacles when the clutter level of the scene interface is less than the second threshold.

[0068] In some embodiments of this application, the electronic device can set a first threshold and a second threshold according to actual needs. The first threshold is greater than the second threshold. For example, the first threshold can be set to 0.7 and the second threshold can be set to 0.3. This application does not impose specific limitations on this.

[0069] In some embodiments of this application, when the clutter level of the scene interface exceeds a first threshold, it can be indicated that the clutter level of the scene interface is high. The electronic device acquires the object identifier corresponding to the dynamic obstacle. Different dynamic obstacles correspond to different object identifiers. For example, the object identifier corresponding to the dynamic obstacle "vehicle" is different from the object identifier corresponding to the dynamic obstacle "pedestrian". Figure 5 As shown in Table 50, at the high level, the electronic device replaces the visual elements corresponding to dynamic obstacles in the scene interface with the object identifiers corresponding to the dynamic obstacles.

[0070] In some embodiments of this application, when the clutter level of the scene interface is less than or equal to a first threshold and greater than or equal to a second threshold, the clutter level of the scene interface can be represented as medium. For example... Figure 5 As shown in Table 50 at the medium level, the electronic device can make the visual elements corresponding to dynamic obstacles transparent. For example, the electronic device adjusts the transparency of the visual elements corresponding to dynamic obstacles.

[0071] In some embodiments of this application, when the clutter level of the scene interface is less than a second threshold, it can be indicated that the clutter level of the scene interface is low. For example... Figure 5 As shown in Table 50, at the low level, the electronic device does not adjust the visual elements corresponding to dynamic obstacles.

[0072] In this embodiment, when the clutter level of the scene interface is greater than a first threshold, the visual elements corresponding to the dynamic obstacles are updated according to the object identifiers of the dynamic obstacles, which can improve the orderliness of the visual elements corresponding to the dynamic obstacles. When the clutter level of the scene interface is less than or equal to the first threshold and greater than or equal to the second threshold, the transparency of the visual elements corresponding to the dynamic obstacles is adjusted, which can not only improve the aesthetics of the scene interface, but also broaden the driver's field of vision.

[0073] In at least one embodiment of this application, the visual elements further include a visual element corresponding to a first static obstacle in the sensing object and / or a visual element corresponding to a second static obstacle in the sensing object, wherein the second static obstacle is used to indicate the scene in which the vehicle is located.

[0074] In at least one embodiment of this application, the electronic device performs perspective processing on the visual elements corresponding to the first static obstacle. For example, the electronic device can perform perspective transformation on the visual elements corresponding to the first static obstacle. By performing perspective processing on the visual elements corresponding to the first static obstacle, the embodiments of this application can improve the aesthetics of the scene interface.

[0075] In other embodiments of this application, the electronic device can enhance at least one of the brightness, hue, sharpness, and saturation of the visual elements corresponding to the second static obstacle. By enhancing at least one of the brightness, hue, sharpness, and saturation of the visual elements corresponding to the second static obstacle, the embodiments of this application can improve the visual cues of the visual elements corresponding to the second static obstacle and optimize the driving experience.

[0076] In at least one embodiment of this application, before adjusting the display parameters of a visual element, the electronic device can perform semantic analysis on the perceived image of the perceived object corresponding to the visual element and the perceived information of the perceived object corresponding to the visual element to obtain the object category of the perceived object corresponding to the visual element. The perceived information of the perceived object may include, but is not limited to: the distance between the perceived object and the vehicle, and the sensors that captured the perceived information.

[0077] In at least one embodiment of this application, the electronic device can adjust the object weight of the perceived object corresponding to the visual element according to the scene in which the vehicle is located and the object category of the perceived object corresponding to the visual element. For example, assuming the scene in which the vehicle is located is an urban road scene during vehicle driving, the object weight of the perceived object "pedestrian" can be adjusted to 1.5, and the object weight of the perceived object "vehicle" can be adjusted to 1.3; assuming the scene in which the vehicle is located is a parking lot scene, the object weight of the perceived object "pillar" can be adjusted to 1.6.

[0078] In at least one embodiment of this application, the electronic device can set and adjust preset weights according to actual needs. The electronic device can delete visual elements corresponding to perceived objects whose object weights are less than the preset weights in the scene interface. By deleting visual elements of perceived objects with smaller object weights, embodiments of this application can improve the display effect of the scene interface.

[0079] In at least one embodiment of this application, after adjusting the display parameters of visual elements, the electronic device performs illumination consistency processing on the visual elements in the adjusted scene interface based on the environmental information of the vehicle's environment.

[0080] In some embodiments of this application, the electronic device can pre-train a deep learning model, and this application does not limit the network structure of the deep learning model. The electronic device uses the deep learning model to encode the lighting parameters in the environmental information to obtain the lighting features of the vehicle's environment. The electronic device uses the deep learning model to encode the visual elements in the adjusted scene interface to obtain element features. The electronic device fuses the lighting features and element features to obtain fused features. In one example, the electronic device can add the lighting features and element features to obtain fused features. In another example, the electronic device can concatenate the lighting features and element features to obtain fused features.

[0081] Electronic devices perform illumination consistency processing on visual elements in the adjusted scene interface by transforming fused features. This application embodiment, through illumination consistency processing on visual elements in the adjusted scene interface, can restore realistic scenes from the real world, thereby improving the realism and naturalness of the adjusted scene interface.

[0082] In other embodiments of this application, the electronic device adjusts the color consistency of visual elements in the adjusted scene interface based on an environmental image of the vehicle's surroundings. Exemplarily, the electronic device extracts color features from the environmental image and adjusts the color consistency of visual elements in the adjusted scene interface based on these color features. By adjusting the color consistency of visual elements in the adjusted scene interface, the embodiments of this application make the colors of the adjusted scene interface more harmonious, enhancing the overall display effect of the adjusted scene interface.

[0083] In other embodiments of this application, the electronic device generates a gradient mask based on the mask features of the adjusted scene interface, and renders the adjusted scene interface according to the gradient mask.

[0084] In some embodiments of this application, the electronic device identifies the visual elements of the adjusted scene interface, determines the background area and foreground area of ​​the adjusted scene interface, and generates mask features based on the background area and foreground area. The pixel value corresponding to the foreground area can be 255, and the pixel value corresponding to the background area can be 0.

[0085] In some embodiments of this application, the electronic device calculates the distance from each pixel in the mask feature to the nearest edge, and maps the distance between each pixel and the nearest edge to the gray value corresponding to each pixel to obtain a gradient mask.

[0086] In some embodiments of this application, the electronic device renders the adjusted scene interface by merging a gradient mask with the adjusted scene interface.

[0087] This application embodiment renders the adjusted scene interface using a gradient mask, which can effectively eliminate the abruptness of the adjusted scene interface boundary, thereby improving the visual effect.

[0088] S204 displays the adjusted scene interface.

[0089] In at least one embodiment of this application, the electronic device can display an adjusted scene interface on the vehicle's display screen.

[0090] In several embodiments of this application, the scene interface of the perceived object can be displayed accordingly based on the perceived object identified by the vehicle. The clutter level of the scene interface can be quantified based on the visual elements in the scene interface. Then, the display parameters of the visual elements can be adjusted accordingly based on the clutter level of the scene interface to improve the orderliness of the visual elements in the adjusted scene interface, thereby improving the user experience.

[0091] like Figure 3 The diagram shown is a detailed flowchart of preprocessing a perceived object according to an embodiment of this application. The order of steps in this flowchart can be changed, and some steps can be omitted, depending on different requirements.

[0092] S301, based on the number of times each perceived object appears at multiple times, filter multiple perceived objects, and determine the first object set based on the perceived objects obtained after filtering.

[0093] In at least one embodiment of this application, to avoid flickering of visual elements, the electronic device can filter multiple sensing objects. For example, the sensing objects identified by the vehicle may include those identified by the vehicle at multiple times. For instance, the sensing objects identified by the vehicle at 10:00 include vehicle A1 and pedestrian B1; the sensing objects identified by the vehicle at 10:01 include vehicle A1, vehicle A2, and pedestrian B1; and the sensing objects identified by the vehicle at 10:02 include vehicle A1 and pedestrian B1. The electronic device can count the number of times each sensing object appears at multiple times. Continuing with the above example, the count shows that sensing object "vehicle A1" appears 3 times, sensing object "vehicle A2" appears 1 time, and sensing object "pedestrian B1" appears 3 times.

[0094] The electronic device calculates the frequency of occurrence of each sensed object based on the number of times each object appears at multiple moments. The frequency of occurrence of any sensed object can be expressed as: , It can represent the frequency of occurrence of any perceived object. It can represent the number of times any perceived object appears at multiple moments. This can represent the number of timestamps corresponding to multiple moments. Continuing with the example above, the number of timestamps corresponding to multiple moments is 3, the frequency of occurrence of the perceived object "vehicle A1" is 3 / 3 = 1, and the frequency of occurrence of the perceived object "vehicle A2" is 1 / 3.

[0095] The electronic device can set and adjust a first preset ratio according to actual needs. For example, the first preset ratio can be set to 0.8, and the electronic device will filter out sensing objects with a frequency lower than the first preset ratio. Continuing the above example, the electronic device can filter out sensing object "vehicle A2". The electronic device determines a first object set based on the filtered sensing objects. Continuing the above example, the first object set may include: sensing object "vehicle A1" and sensing object "pedestrian B1".

[0096] S302, based on the positional overlap rate of any two sensing objects in the first object set, perform de-overlap processing on the sensing objects in the first object set to obtain the second object set.

[0097] In at least one embodiment of this application, the electronic device can construct a vehicle coordinate system based on any reference point of the vehicle as the origin. The electronic device determines the position coordinates of each sensing object in the first object set within the vehicle coordinate system. Based on the position coordinates of any two sensing objects in the first object set, the electronic device determines the position overlap rate of any two sensing objects in the first object set.

[0098] For example, the electronic device determines the circumscribed quadrilateral of each sensing object in the first object set based on the position coordinates of each sensing object in the first object set. The electronic device then determines the intersection region of any two sensing objects in the first object set based on their position coordinates. Finally, the electronic device calculates the ratio of the area of ​​the intersection region to the total area of ​​the circumscribed quadrilaterals of any two sensing objects in the first object set to obtain the positional overlap rate of the two sensing objects in the first object set.

[0099] In at least one embodiment of this application, the electronic device can set and adjust the second preset ratio according to actual needs; for example, the second preset ratio can be set to 0.7. For any two sensing objects in the first object set whose position overlap rate is greater than or equal to the second preset ratio, the electronic device filters the two sensing objects to obtain the second object set.

[0100] In one example, for any two sensed objects in the first object set whose positional overlap rate is greater than or equal to a second preset ratio, the electronic device determines the distance between the two sensed objects and the vehicle, and filters out the sensed objects that are farther away to obtain a second object set. In another example, for any two sensed objects in the first object set whose positional overlap rate is greater than or equal to a second preset ratio, the electronic device determines the target time when the vehicle identifies the two sensed objects, and filters out the sensed objects whose target time is later to obtain a second object set.

[0101] For any two sensing objects in the first object set whose position overlap rate is less than a second preset ratio, the electronic device moves the position coordinates of the two sensing objects.

[0102] This application embodiment quantifies the positional overlap rate of any two sensing objects in the first object set to obtain the overlap situation of any two sensing objects in the first object set. Then, based on the positional overlap rate of any two sensing objects in the first object set, the sensing objects in the first object set are de-overlapped, which can avoid the appearance of sensing objects in the second object set that violate the rules of the real world. For example, in the real world, it is impossible for two vehicles to be parked in the same parking space at the same time.

[0103] In at least one embodiment of this application, before performing de-overlap processing on the sensed objects in the first object set, the electronic device filters the position coordinates of the sensed objects in the first object set. By filtering the position coordinates, this embodiment of the application can reduce abrupt changes in the sensed objects, making the position information of the sensed objects smoother.

[0104] S303, for any two perceived objects in the second object set, use the perceived object with higher confidence to calibrate the perceived object with lower confidence to obtain the preprocessed perceived object.

[0105] In at least one embodiment of this application, the electronic device determines the acquisition sensor for each sensed object in the second object set. The electronic device can preset the accuracy corresponding to each acquisition sensor based on experience. For example, the accuracy corresponding to a camera is 0.8, and the accuracy corresponding to a radar is 0.7. The accuracy corresponding to each acquisition sensor can be adjusted according to actual needs, and this application does not impose any limitations on this.

[0106] The electronic device determines the distance between each sensed object in the second object set and the vehicle. Based on the accuracy of the sensor for each sensed object and the distance between each sensed object and the vehicle, the electronic device determines the confidence level of each sensed object in the second object set. For example, the electronic device performs a weighted sum operation on the accuracy of the sensor for each sensed object and the distance between each sensed object and the vehicle to obtain the confidence level of each sensed object in the second object set.

[0107] In at least one embodiment of this application, for any two sensed objects in the second object set, the electronic device uses the sensed object with higher confidence to calibrate the sensed object with lower confidence, thereby obtaining a preprocessed sensed object. Exemplarily, during the calibration of the sensed object with lower confidence, the electronic device updates the position of the sensed object with lower confidence based on the position of the sensed object with higher confidence.

[0108] For example, during vehicle operation, if the perceived object "lane line" is parallel to the perceived object "zebra crossing", and the confidence level of the perceived object "lane line" is higher than that of the perceived object "zebra crossing", the electronic device can adjust the position of the perceived object "zebra crossing" so that the perceived object "lane line" and the perceived object "zebra crossing" are not parallel.

[0109] For example, during the parking process, the confidence level of the perceived object "parking space" is higher than that of the perceived object "pillar". The electronic device can use the perceived object "parking space" to calibrate the perceived object "pillar".

[0110] In at least one embodiment of this application, the electronic device can set and adjust a third preset ratio according to actual needs; for example, the third preset ratio can be set to 0.7. The electronic device will mutually calibrate any two sensed objects with a confidence level greater than the third preset ratio. For example, the electronic device can mutually calibrate the sensed object "lane line" and the sensed object "parking space".

[0111] This application's embodiments filter multiple sensing objects by counting the number of times each sensing object appears at multiple times. This effectively reduces flickering of visual elements corresponding to sensing objects in the scene interface, thereby improving the stability and smoothness of the scene interface. By performing de-overlap processing on the sensing objects in the first object set, abnormalities in the sensing objects in the second object set can be avoided, thus improving the accuracy of the second object set. By using sensing objects with higher confidence to calibrate sensing objects with lower confidence, the accuracy and reliability of the preprocessed sensing objects can be further improved.

[0112] like Figure 6 The diagram shown is a flowchart of an interface optimization method provided in another embodiment of this application. Depending on different requirements, the order of the steps in this flowchart can be changed, and some steps can be omitted.

[0113] S601 displays the scene interface corresponding to the perceived object based on the vehicle's identification.

[0114] S602, calculate the clutter level of the scene interface based on the visual elements in the scene interface.

[0115] S603 adjusts the display parameters of visual elements based on the clutter level of the scene interface.

[0116] For details on steps S601 to S603, please refer to the above text. Figure 2 The detailed descriptions of steps S201 to S203 are not repeated here.

[0117] S604 determines the user's travel scenario based on vehicle driving information and user behavior characteristics.

[0118] In at least one embodiment of this application, the vehicle's driving information may include, but is not limited to, the vehicle's departure location, departure time, and object information of landmark objects.

[0119] In at least one embodiment of this application, the user's behavioral characteristics may include, but are not limited to: the user's navigation destination during a preset time period and the time point corresponding to the user's navigation destination.

[0120] In at least one embodiment of this application, the electronic device can pre-train a scene recognition model, which can use a trajectory clustering algorithm to identify the user's travel scenario. For example, the electronic device inputs vehicle driving information and user behavioral characteristics into the scene recognition model to obtain a predicted scenario and the corresponding confidence level.

[0121] In at least one embodiment of this application, the electronic device may also set a first confidence threshold and a second confidence level according to actual needs, wherein the first confidence threshold is greater than the second confidence threshold.

[0122] In one example, if the confidence level of the predicted scenario is greater than the first confidence threshold, the predicted scenario is identified as a travel scenario. In another example, if the confidence level of the predicted scenario is less than or equal to the first confidence threshold, and greater than the second confidence threshold, a large model is used to generate a question based on the predicted scenario and obtain the user's response to the question to determine the travel scenario. For example, if the predicted scenario is "on the way to work," the question could be: "Are you currently on your way to work? Should I turn on ambient music for you?" In another example, if the confidence level of the predicted scenario is less than or equal to the second confidence threshold, vehicle driving information and user behavioral characteristics are re-acquired for scenario identification.

[0123] S605, adjusts the background color of the adjusted scene interface according to the travel scenario.

[0124] In at least one embodiment of this application, the electronic device may pre-store the correspondence between travel scenarios and background colors of the scenario interface. The electronic device then adjusts the interface by obtaining the background color that matches the travel scenario based on the pre-stored correspondence.

[0125] In other embodiments of this application, the electronic device can also adjust the vehicle's ambient music and background lighting according to the travel scenario.

[0126] S606 displays the adjusted scene interface.

[0127] In at least one embodiment of this application, the electronic device can display an adjusted scene interface on the vehicle's display screen.

[0128] This application embodiment can accurately determine the user's travel scenario by combining vehicle driving information and user behavior characteristics. Then, based on the travel scenario, the background color of the adjusted scenario interface can be reasonably adjusted to improve the matching degree between the scenario interface and the user's emotions.

[0129] like Figure 7 The diagram shown is a framework schematic of an interface optimization method provided in an embodiment of this application.

[0130] In some embodiments of this application, the electronic device can preprocess the sensing elements identified by the vehicle; these sensing elements can also be referred to as sensing objects. Exemplarily, the electronic device performs multi-frame confirmation processing on the sensing elements. The method of multi-frame confirmation processing for sensing elements by the electronic device can be found in [reference needed]. Figure 3The details of step S301 will not be repeated here. The electronic device can also perform de-overlap processing on the sensing elements; the method for de-overlap processing of sensing elements by the electronic device can be found in [link to relevant documentation]. Figure 3 The details of step S302 will not be repeated here. The electronic device can also perform de-jitter processing on the sensing elements; the method for performing de-jitter processing on the sensing elements can be found in [link to relevant documentation]. Figure 3 The method of filtering the position coordinates of the perceived objects in the first object set in step S302 will not be described again here.

[0131] In some embodiments of this application, the electronic device can also perform inter-element calibration of the sensing elements. For example, the electronic device can perform inter-element calibration of the sensing element "zebra crossing" and the sensing element "lane line". The electronic device can also perform inter-element calibration of the sensing element "parking space" and the sensing element "lane line".

[0132] In some embodiments of this application, the electronic device performs scene modeling based on sensor elements. Exemplarily, the electronic device performs template matching integration based on sensor elements to obtain a scene interface. For details on how the electronic device obtains the scene interface, please refer to... Figure 2 The details of step S201 will not be repeated here. The electronic device performs virtual-real blending on the visual elements in the scene interface to obtain the adjusted scene interface. The method by which the electronic device performs virtual-real blending on the visual elements in the scene interface can be found in [reference needed]. Figure 2 The details of step S203 will not be repeated here.

[0133] Electronic devices can also identify user scenarios, also known as travel scenarios. Based on these scenarios, electronic devices adjust the atmosphere of their interface.

[0134] In some embodiments of this application, the electronic device displays an adjusted scene interface on the vehicle's display screen.

[0135] In several embodiments of this application, the electronic device, based on a preset interface template, can quickly integrate perceived elements to obtain a scene interface, and then use a virtual-real integration method to improve the aesthetics and accuracy of the display. Furthermore, by inferring and recognizing the user's usage scenario, a corresponding atmosphere can be created, enhancing the user experience.

[0136] like Figure 8 The diagram shown is a framework schematic of a method for adjusting the display parameters of visual elements according to an embodiment of this application.

[0137] In some embodiments of this application, the vehicle's camera can capture a video stream of the vehicle's environment, and the electronic device can input the raw video stream captured by the vehicle into a preprocessing module. The electronic device can use the preprocessing module to preprocess the images in the raw video stream. For example, the electronic device can reduce the resolution of the images in the raw video stream, thereby improving interface optimization efficiency. The electronic device can also preprocess perceptual elements of the images in the raw video stream; these perceptual elements can also be called perceptual objects. The method by which the electronic device preprocesses perceptual elements can be found in [reference needed]. Figure 3 The process is shown below.

[0138] In some embodiments of this application, the electronic device performs scene understanding and semantic segmentation on the images and perceptual information of perceptual elements in the original video stream to obtain the category corresponding to the perceptual element. The method by which the electronic device performs scene understanding and semantic segmentation can be referred to... Figure 2 The relevant description of the semantic analysis performed in step S203 is not described again in this application.

[0139] In some embodiments of this application, the electronic device adjusts the weights of the sensing elements based on their corresponding categories. The electronic device then filters the sensing elements based on the adjusted weights.

[0140] In some embodiments of this application, the electronic device performs clutter assessment and obstacle recognition on the perceived elements. The electronic device can also monitor the driver's state and the vehicle's environment in real time. Based on the driver's state and the dynamic environment, the electronic device can dynamically adjust the parameters of the scene interface and perform virtual-real fusion decision processing on the scene interface based on the clutter assessment results.

[0141] In one example, the electronic device can set a first threshold and a second threshold according to actual needs, with the first threshold being greater than the second threshold. If the clutter level of the scene interface exceeds the first threshold, the electronic device replaces the perceived elements in the scene interface with virtual elements. Through virtual element matching and layout, the electronic device can achieve scene repair and synthesis of the scene interface.

[0142] In another example, if the clutter level of the scene interface is less than or equal to a first threshold and greater than or equal to a second threshold, the electronic device can achieve scene completion and perspective rendering of the scene interface by making the obstacles transparent.

[0143] In some embodiments of this application, the electronic device performs post-processing and optimization on the adjusted scene interface to obtain an enhanced output video.

[0144] This application embodiment uses virtual element replacement, perspective and other methods to perform virtual-real integration processing on the scene interface, and at the same time makes obstacles and walls that obstruct the view transparent, which can improve the aesthetics of the interface and improve the driver's vision.

[0145] like Figure 9 The diagram shown is a framework schematic of a method for adjusting the display parameters of a visual element according to another embodiment of this application.

[0146] In some embodiments of this application, the electronic device can collect multi-source data, which may include, but is not limited to, departure location, departure time, user behavior characteristics, and environmental characteristics.

[0147] In some embodiments of this application, the electronic device learns and recognizes scene patterns based on multi-source data. For example, the electronic device can use a trajectory clustering algorithm to determine a target location, which may indicate a location frequently visited by the user. The electronic device can use a scene inference engine to identify the user's travel scenarios. The scene inference engine can perform cross-scenario continuity recognition of the user's travel scenarios; for example, the travel scenario "taking a child to school" and the travel scenario "commuting to work" can constitute a continuous cross-scenario.

[0148] In some embodiments of this application, the electronic device performs a confidence assessment on the travel scenario output by the scene inference engine.

[0149] In some embodiments of this application, the electronic device can set a first confidence threshold and a second confidence threshold according to actual needs, wherein the first confidence threshold is greater than the second confidence threshold.

[0150] In one example, if the confidence level of the travel scenario output by the scenario inference engine is greater than the first confidence threshold, the scenario interface is adjusted according to the scenario.

[0151] In another example, if the confidence level of the travel scenario output by the scenario inference engine is less than or equal to the first confidence level threshold, and the confidence level of the travel scenario output by the scenario inference engine is greater than the second confidence level threshold, the electronic device performs multimodal interaction confirmation on the travel scenario output by the scenario inference engine, and adjusts the scenario interface according to the scenario based on the user feedback.

[0152] In another example, if the confidence level of the travel scenario output by the scenario inference engine is less than or equal to the second confidence threshold, the electronic device will re-collect multi-source data for continuous learning.

[0153] This application embodiment can adjust the atmosphere scheme to match the user's emotions based on the user's travel scenario.

[0154] like Figure 10The diagram shown is a functional block diagram of an interface optimization device provided in an embodiment of this application. The interface optimization device 11 includes a display unit 1010, a calculation unit 1011, an optimization unit 1012, a preprocessing unit 1013, and a determination unit 1014. The module / unit referred to in this application refers to a module / unit that can be processed by a processor (e.g., ...). Figure 11 A series of computer program segments acquired by the processor 1101 shown, and capable of performing a fixed function, which are stored in memory (e.g., memory). Figure 11 In the memory 1102 shown.

[0155] In one embodiment, the display unit 1010 is used to display the scene interface corresponding to the perceived object based on the perceived object identified by the vehicle; the calculation unit 1011 is used to calculate the clutter level of the scene interface based on the visual elements in the scene interface; the optimization unit 1012 is used to adjust the display parameters of the visual elements based on the clutter level of the scene interface; the display unit 1010 is also used to display the adjusted scene interface.

[0156] In one embodiment, the sensing objects include sensing objects identified by the vehicle at multiple times. The preprocessing unit 1013 is used to preprocess the sensing objects. The preprocessing includes: filtering multiple sensing objects based on the number of times each sensing object appears at multiple times; determining a first object set based on the filtered sensing objects; performing de-overlap processing on the sensing objects in the first object set based on the position overlap rate of any two sensing objects in the first object set to obtain a second object set; and calibrating the sensing objects with lower confidence using the sensing objects with higher confidence for any two sensing objects in the second object set to obtain preprocessed sensing objects.

[0157] In one embodiment, the sensing object includes a landmark object. The display unit 1010 is specifically used to: obtain an interface template that matches the landmark object; and fill the object area of ​​the interface template with the visual elements corresponding to the sensing object to obtain the scene interface.

[0158] In one embodiment, the calculation unit 1011 is specifically used to: calculate the disorder of visual elements based on the area ratio of visual elements, spatial disorder, and matching degree between visual elements and scene interface; and calculate the disorder of scene interface based on the display position of visual elements on scene interface and the disorder of visual elements.

[0159] In one embodiment, the visual elements include visual elements corresponding to dynamic obstacles in the perceived object. The optimization unit 1012 is specifically used for: updating the visual elements corresponding to the dynamic obstacles according to the object identifier of the dynamic obstacles when the clutter level of the scene interface is greater than a first threshold; adjusting the transparency of the visual elements corresponding to the dynamic obstacles when the clutter level of the scene interface is less than or equal to the first threshold and greater than or equal to a second threshold, wherein the first threshold is greater than the second threshold; and not adjusting the visual elements corresponding to the dynamic obstacles when the clutter level of the scene interface is less than the second threshold.

[0160] In one embodiment, the visual elements include visual elements corresponding to a first static obstacle in the sensing object and / or visual elements corresponding to a second static obstacle in the sensing object. The second static obstacle is used to indicate the scene in which the vehicle is located. The optimization unit 1012 is also used to perform perspective processing on the visual elements corresponding to the first static obstacle and / or enhance at least one of the brightness, hue, sharpness and saturation of the visual elements corresponding to the second static obstacle.

[0161] In one embodiment, after adjusting the display parameters of the visual elements, the optimization unit 1012 is further configured to perform illumination consistency processing on the visual elements in the adjusted scene interface based on the environmental information of the vehicle's environment; and / or perform color consistency adjustment on the visual elements in the adjusted scene interface based on the environmental image of the vehicle's environment; and / or generate a gradient mask based on the mask features of the adjusted scene interface, and render the adjusted scene interface according to the gradient mask.

[0162] In one embodiment, the determining unit 1014 is used to determine the user's travel scenario based on the vehicle's driving information and the user's behavioral characteristics; the optimizing unit 1012 is also used to adjust the background color of the adjusted scenario interface based on the travel scenario.

[0163] In several embodiments of this application, the scene interface of the perceived object can be displayed accordingly based on the perceived object identified by the vehicle. The clutter level of the scene interface can be quantified based on the visual elements in the scene interface. Then, the display parameters of the visual elements can be adjusted accordingly based on the clutter level of the scene interface to improve the orderliness of the visual elements in the adjusted scene interface, thereby improving the user experience.

[0164] like Figure 11 The diagram shown is a schematic representation of the structure of an electronic device that implements the interface optimization method of this application.

[0165] In one embodiment of this application, the electronic device 100 includes, but is not limited to, a memory 1102, a processor 1101, and a computer program, such as an interface optimization program, stored in the memory 1102 and executable on the processor 1101.

[0166] Those skilled in the art will understand that the schematic diagram is merely an example of the electronic device 100 and does not constitute a limitation on the electronic device 100. It may include more or fewer components than shown, or combine certain components, or different components. For example, the electronic device 100 may also include input / output devices, network access devices, buses, etc.

[0167] Processor 1101 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. Processor 1101 is the computing core and control center of electronic device 100, connecting various parts of electronic device 100 through various interfaces and lines, and acquiring the operating system of electronic device 100 and various installed application programs and program code.

[0168] Processor 1101 acquires the operating system and various installed applications of electronic device 100. Processor 1101 acquires these applications to implement the steps in the various interface optimization method embodiments described above, for example... Figures 2 to 3 , Figures 6 to 9 The steps are shown.

[0169] The memory 1102 can be used to store computer programs and / or modules. The processor 1101 implements various functions of the electronic device 100 by running or retrieving the computer programs and / or modules stored in the memory 1102, and by calling the data stored in the memory 1102. The memory 1102 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device, etc. In addition, the memory 1102 may include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0170] The memory 1102 can be the external memory and / or internal memory of the electronic device 100. Furthermore, the memory 1102 can be a memory in physical form, such as a memory stick, a TF card (Trans-flash Card), etc.

[0171] If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent workpieces, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above.

[0172] Computer programs include computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. Computer-readable media can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), and random access memory (RAM).

[0173] For example, a computer program can be divided into one or more modules / units, one or more of which are stored in memory 1102 and executed by processor 1101 to complete this application. One or more modules / units can be a series of computer program segments capable of performing specific functions, and these computer program segments describe the execution process of the computer program in electronic device 100. For example, the computer program can be divided into a display unit 1010, a calculation unit 1011, an optimization unit 1012, a preprocessing unit 1013, and a determination unit 1014.

[0174] For detailed information on the functions of each module / unit, please refer to the above text. Figures 2 to 3 , Figures 6 to 9 The detailed description will not be repeated here.

[0175] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0176] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0177] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0178] Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of this application is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be embraced within this application. No appended diagram markings in the claims should be construed as limiting the scope of the claims.

[0179] Furthermore, it is clear that the word "including" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices can also be implemented by a single unit or device through software or hardware. Terms such as "first," "second," etc., are used to indicate names and do not indicate any specific order.

[0180] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit it. Although this application has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of this application without departing from the spirit and scope of the technical solutions of this application.

Claims

1. An interface optimization method, characterized in that, The method includes: Based on the perceived objects identified by the vehicle, the scene interface corresponding to the perceived objects is displayed; Calculate the clutter level of the scene interface based on the visual elements in the scene interface; The display parameters of the visual elements are adjusted according to the clutter level of the scene interface. The adjusted scene interface is displayed.

2. The interface optimization method according to claim 1, characterized in that, The sensing objects include sensing objects identified by the vehicle at multiple times, and the method further includes: preprocessing the sensing objects, the preprocessing including: Based on the number of times each perceived object appears at the multiple times, multiple perceived objects are filtered, and a first set of objects is determined based on the perceived objects obtained after filtering. Based on the positional overlap rate of any two sensing objects in the first object set, the sensing objects in the first object set are de-overlapped to obtain the second object set. For any two perceived objects in the second object set, the perceived object with the higher confidence is used to calibrate the perceived object with the lower confidence, resulting in a preprocessed perceived object.

3. The interface optimization method according to claim 1, characterized in that, The sensed objects include iconic objects. The sensed objects identified based on vehicles, and the scene interface corresponding to the sensed objects, include: Obtain the interface template that matches the iconic object; The visual elements corresponding to the perceived object are filled into the object area of ​​the interface template to obtain the scene interface.

4. The interface optimization method according to claim 1, characterized in that, The step of calculating the clutter level of the scene interface based on the visual elements in the scene interface includes: The disorder of the visual element is calculated based on its area ratio, spatial disorder, and matching degree between the visual element and the scene interface. The clutter level of the scene interface is calculated based on the display position of the visual element in the scene interface and the clutter level of the visual element.

5. The interface optimization method according to claim 1, characterized in that, The visual elements include visual elements corresponding to dynamic obstacles in the perceived object. Adjusting the display parameters of the visual elements based on the clutter level of the scene interface includes: When the clutter level of the scene interface exceeds a first threshold, the visual elements corresponding to the dynamic obstacle are updated according to the object identifier corresponding to the dynamic obstacle. When the clutter level of the scene interface is less than or equal to the first threshold and the clutter level of the scene interface is greater than or equal to the second threshold, the transparency of the visual elements corresponding to the dynamic obstacle is adjusted, where the first threshold is greater than the second threshold. If the clutter level of the scene interface is less than the second threshold, the visual elements corresponding to the dynamic obstacles will not be adjusted.

6. The interface optimization method according to claim 1, characterized in that, The visual elements include visual elements corresponding to a first static obstacle in the sensing object and / or visual elements corresponding to a second static obstacle in the sensing object, wherein the second static obstacle is used to indicate the scene in which the vehicle is located, and the method further includes: Perspective processing is applied to the visual elements corresponding to the first static obstacle; and / or Enhance at least one of the brightness, hue, sharpness, and saturation of the visual element corresponding to the second static obstacle.

7. The interface optimization method according to any one of claims 1 to 6, characterized in that, After adjusting the display parameters of the visual elements, the method further includes: Based on the environmental information of the vehicle's location, the visual elements in the adjusted scene interface undergo illumination consistency processing; and / or Based on the environmental image of the vehicle's location, the visual elements in the adjusted scene interface are color-consistently adjusted; and / or Based on the mask features of the adjusted scene interface, a gradient mask is generated, and the adjusted scene interface is rendered according to the gradient mask.

8. The interface optimization method according to any one of claims 1 to 6, characterized in that, The method further includes: Based on the vehicle's driving information and the user's behavioral characteristics, the user's travel scenario is determined; Based on the described travel scenario, the background color of the adjusted scenario interface is modified.

9. An electronic device, characterized in that, include: A memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the interface optimization method as described in any one of claims 1 to 8.

10. A vehicle, characterized in that, The vehicle is equipped with the electronic equipment as described in claim 9.

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