Intelligent voice vehicle lamp control method, vehicle machine and program product

Abstract

The invention discloses an intelligent voice vehicle lamp control method, a vehicle machine and a program product, and relates to the technical field of intelligent light control, and the method comprises the steps: continuously and synchronously collecting scene parameters fed back by a whole vehicle; recording a voice instruction actively sent by a driver every time and actual light parameters which are finally executed and subjected to situation fusion optimization; establishing a driver preference database according to the historically recorded voice instructions and the corresponding light parameters; and when a voice instruction is input, carrying out fusion decision making on the obtained voice instruction and the current scene parameters, and avoiding the decision with potential safety hazards. According to the intelligent voice vehicle lamp control method, the vehicle machine and the program product provided by the invention, the driving safety can be greatly improved, the user preference can be learned, the personalized lighting service can be provided, the more effective human-vehicle interaction service can be provided, and the user interaction feeling can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent light control, in particular to an intelligent voice vehicle light control method, a vehicle machine and a program product. BACKGROUND

[0002] With the development of vehicle light technology, existing vehicles have been able to directly control the on-off of vehicle lights through simple instructions (such as "turn on high beam" and "turn off ambient light"). However, this technology is only one-way interaction from command to execution, and lacks understanding of the vehicle driving state, external environment and personnel state, which may trigger unsafe or unreasonable operations. SUMMARY

[0003] The purpose of the present application is to provide an intelligent voice vehicle light control method, a vehicle machine and a program product, which greatly improves driving safety, learns user preferences, provides personalized lighting services, provides more effective human-vehicle interaction services, and improves user interaction experience.

[0004] The present application provides the following solutions:

[0005] According to one aspect of the present application, an intelligent voice vehicle light control method is provided, which comprises:

[0006] Continuously and synchronously collecting scene parameters from vehicle feedback;

[0007] Recording each time the driver actively issues a voice instruction and the actual light parameter after situation fusion optimization that is finally executed;

[0008] Establishing a driver preference database according to the historically recorded voice instructions and corresponding light parameters;

[0009] When a voice instruction is input, the obtained voice instruction is fused and decided with the current scene parameters, and decisions with safety hazards are avoided.

[0010] Optionally, establishing a driver preference database according to the historically recorded voice instructions and corresponding light parameters comprises:

[0011] Counting the finally executed light parameters under the same voice instruction condition in the historical record;

[0012] Recording the light parameter with the most occurrences under the same voice instruction condition into the driver preference database.

[0013] Optionally, counting the finally executed light parameters under the same voice instruction condition in the historical record comprises:

[0014] Counting the final executed light parameters under the same voice instruction and the same scene parameter condition in the history record.

[0015] Optionally, the most frequently occurred light parameter under the same voice instruction is recorded in the driver preference database, including:

[0016] Counting the occurrence times of all light parameter combinations under the same voice instruction;

[0017] Writing the top several light parameter combinations in the ranking order into the driver preference database.

[0018] Optionally, when a voice instruction is input, the obtained voice instruction is fused with the current scene parameter for decision making, and the decision making with safety hazards is avoided, including:

[0019] Classifying the current scene parameter into a typical scene type;

[0020] Obtaining a dangerous action list corresponding to the typical scene type;

[0021] Filtering the action corresponding to the voice instruction by using the dangerous action list.

[0022] Optionally, when a voice instruction is input, the obtained voice instruction is fused with the current scene parameter for decision making, and the decision making with safety hazards is avoided, further including:

[0023] Obtaining an associated action of the action corresponding to the voice instruction under the typical scene type;

[0024] Executing the associated action of the action corresponding to the voice instruction.

[0025] Optionally, when a voice instruction is input, the obtained voice instruction is fused with the current scene parameter for decision making, and the decision making with safety hazards is avoided, including:

[0026] Querying the driver preference database according to the current scene parameter;

[0027] Executing the action associated with the input voice instruction found in the driver preference database.

[0028] According to the two aspects of the present application, an intelligent voice car light control device is provided, and the intelligent voice car light control device includes:

[0029] A parameter acquisition module is configured to continuously and synchronously acquire scene parameters from vehicle feedback;

[0030] A recording module is configured to record each time the voice instruction actively issued by the driver and the actual light parameter optimized through context fusion and finally executed;

[0031] A database establishing module is configured to establish a driver preference database according to the historically recorded voice instruction and the light parameter corresponding thereto;

[0032] A fusion decision module is configured to, when a voice instruction is input, make a fusion decision on the obtained voice instruction and the current scene parameter, and evade the decision with a safety hazard.

[0033] According to the three aspects of the present application, a car machine is provided, which comprises a processor and a memory, the memory storing a computer program, the computer program being loaded and executed by the processor to implement the intelligent voice car light control method as described above.

[0034] According to the four aspects of the present application, a computer program product is provided, comprising a computer program, the computer program being executed by a processor to implement the intelligent voice car light control method as described above.

[0035] Through the above scheme, the following beneficial technical effects are obtained:

[0036] The intelligent voice car light control method, car machine and program product provided by the present application can combine real-time vehicle data to intelligently analyze and execute voice instructions, greatly improve driving safety, learn user preferences, provide personalized lighting services, provide more effective human-vehicle interaction services, and improve user interaction experience. BRIEF DESCRIPTION OF DRAWINGS

[0037] Figure 1 is a flowchart of the intelligent voice car light control method provided by one or more embodiments of the present application;

[0038] Figure 2 is a flowchart of the database establishing operation in the intelligent voice car light control method provided by one or more embodiments of the present application;

[0039] Figure 3 is a flowchart of the database establishing operation in the intelligent voice car light control method provided by one or more embodiments of the present application;

[0040] Figure 4 is a flowchart of the database establishing operation in the intelligent voice car light control method provided by one or more embodiments of the present application;

[0041] Figure 5 is a flowchart of the fusion decision operation in the intelligent voice car light control method provided by one or more embodiments of the present application;

[0042] Figure 6 is a flowchart of the fusion decision operation in the intelligent voice vehicle light control method provided by one or more embodiments of the present application;

[0043] Figure 7 is a flowchart of the fusion decision operation in the intelligent voice vehicle light control method provided by one or more embodiments of the present application;

[0044] Figure 8 is a structural diagram of the intelligent voice vehicle light control device provided by one or more embodiments of the present application;

[0045] Figure 9 is a structural diagram of the vehicle machine provided by one or more embodiments of the present application. DETAILED DESCRIPTION

[0046] The technical solutions of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0047] Figure 1 is a flowchart of the intelligent voice vehicle light control method provided by one or more embodiments of the present application. Referring to Figure 1 , the intelligent voice vehicle light control method comprises the following operation steps:

[0048] S11, continuously and synchronously collecting scene parameters from the whole vehicle feedback.

[0049] S12, recording each time the driver actively issues a voice instruction and the actual light parameter after the situation fusion optimization which is finally executed.

[0050] S13, establishing a driver preference database according to the historical recorded voice instructions and the corresponding light parameters.

[0051] S14, when there is a voice instruction input, the obtained voice instruction is fused with the current scene parameters for decision making, and the decision making with safety hazards is avoided.

[0052] In various embodiments of the present application, the so-called scene parameters are for the whole vehicle. This means that each specific parameter item contained in the scene parameters is not the scene in which a specific component or a specific subsystem in the vehicle is located, but the scene in which the whole vehicle is located.

[0053] Taking the light intensity parameter in the scene parameters as an example, it is not the light intensity of a certain vehicle light, nor the light intensity under a certain specific working condition, but the overall light intensity of the current environment in which the whole vehicle is located.

[0054] The role of the light intensity parameter in the application of the subsequent steps is to indicate the light condition of the environment where the vehicle is located. In some application scenarios that need to determine whether it is daytime or nighttime, the light intensity parameter plays an important role.

[0055] The collection action of the scene parameters is continuous on the time axis. That is, the collection of the scene parameters is a continuous collection. There will be no case that some or several collected scene parameters only exist at some discontinuous points on the time axis, and there will be a long period of time without specific parameter values.

[0056] In addition, the collection action of the scene parameters is a synchronous collection action. This means that the multiple different collected scene parameters are aligned with each other on the time axis. Knowing the light intensity parameter at a time point can know the vehicle speed parameter at the same time point.

[0057] The role of collecting scene parameters is to make a fusion decision. That is, in various embodiments of the present application, the execution of the voice instruction is not only based on the voice instruction itself, but further needs to consider the specific scene where the vehicle is located when the voice instruction is obtained.

[0058] For example, for the voice instruction of playing music, after considering the scene where the vehicle is located, the execution of the instruction is no longer the unselected and purposeless playing of the target music. Instead, it evolves into the playing of the target music after screening according to the current scene where the vehicle is located.

[0059] For example, if the current time is night and the vehicle is driving at high speed, music that can easily distract the driver should not be played, but music that is soothing and slow-paced should be played, which can make it easier for the driver to focus on driving at night.

[0060] There can be many ways to make a fusion decision.

[0061] The first is to establish a dangerous action list for each scene type. The actual action to be executed is first filtered, that is, screened, using the dangerous action list. If an action belongs to a dangerous action under the current scene category, the action should not be executed.

[0062] The second is to refer to a preference database. Historical data shows that the same action should be executed under the current scene.

[0063] Of course, the fusion decision-making process can also be inferred using mathematical models. Specifically, decision trees, neural networks, and a range of other models can be used to perform the inference. This invention does not limit the specific model used for inference.

[0064] After voice commands undergo fusion decision-making, they are then executed. The first advantage of this is improved driving safety. Commands that pose significant safety risks in the current scenario are no longer executed after fusion decision-making. This strengthens the safety measures during driving.

[0065] Another advantage is the significantly improved personalization of voice command execution. This is particularly evident in command execution based on a driver preference database. Since the command execution results in the preference database are learned from historical data—that is, summarized from driver usage habits—it ensures that the command execution results are what the driver needs.

[0066] In various embodiments of the present invention, in addition to making decisions on the voice commands to be executed in conjunction with environmental parameters, a database of the driver's personal preferences is further established.

[0067] The driver preference database aggregates and statistically analyzes drivers' historical voice command execution data, yielding experiential data on how drivers expect voice commands to be executed in different scenarios. This experiential data allows for future reference in voice command execution, making the execution of voice commands more tailored to user needs and improving the user experience.

[0068] The process of building a driver preference database is essentially a learning process of user voice command habits. The preference database is built by learning the execution results of the same voice commands.

[0069] The learning process described above can be applied not only to learning the execution results of the same voice commands, but also to learning the execution results of the same voice commands in the same scenario.

[0070] Furthermore, in various embodiments of the present invention, the execution results of voice commands are not limited to the execution results of lights. For example, turning the headlights to their brightest setting and flashing the parking lights, etc. The concept of execution results can also be extended to other vehicle components or subsystems. For example, turning on the air conditioning and turning on the heater, etc.

[0071] Figure 2 This is a flowchart of the database establishment operation in the intelligent voice-controlled vehicle lighting method provided in one or more embodiments of the present invention. See also Figure 2Based on historical voice commands and corresponding lighting parameters, a driver preference database is established, including:

[0072] S21, statistically analyze the lighting parameters executed under the same voice command conditions in the historical records.

[0073] S22 records the lighting parameters that appear most frequently under the same voice command conditions into the driver preference database.

[0074] It should be understood that the process of building a preference database is a process of learning from the results of historical voice command execution.

[0075] The learning mentioned here refers to the statistical analysis of different lighting parameters.

[0076] It should be understood that, due to the different actual purposes of the driver and the different scenarios in which the vehicle is located, the same voice command issued at different times will result in different actual vehicle responses.

[0077] In this embodiment, the vehicle system learns the actual response of the vehicle. That is, under the premise of inputting the same voice command, it statistically analyzes the actual lighting parameters obtained, which is the execution result of the voice command.

[0078] It's understandable that the lighting parameters that appear most frequently are the ones that best meet the actual needs of drivers.

[0079] Figure 3 This is a flowchart of the database establishment operation in the intelligent voice-controlled vehicle lighting method provided in one or more embodiments of the present invention. See also Figure 3 Based on historical voice commands and corresponding lighting parameters, a driver preference database is established, including:

[0080] S31: Statistically analyze the lighting parameters executed under the same voice commands and scene parameters in the historical records.

[0081] S32 records the lighting parameters that appear most frequently under the same voice command conditions into the driver preference database.

[0082] The technical solution provided in this embodiment differs from the aforementioned embodiments of this application in that, during the establishment of the preference database, especially in the statistical analysis of lighting parameters, it considers not only statistical analysis based on the same voice commands but also statistical analysis based on the same scene parameters. In other words, the statistical analysis of lighting parameters comprehensively considers two factors: the voice commands input by the driver and the collected scene parameters.

[0083] The reason for considering scenario parameters is mainly to accurately define driver preferences.

[0084] Taking drivers' music preferences as an example, their preferences differ depending on whether it's raining or sunny. For instance, on rainy days, drivers tend to prefer listening to soothing music, while on sunny days, they prefer listening to upbeat music.

[0085] Taking full account of the aforementioned detailed data during the preference database establishment phase is beneficial for accurately capturing driver preferences. With detailed basic data on driver preferences captured, the music actually recommended will be more tailored to user needs in subsequent preference data utilization phases.

[0086] Take, for example, driver behavior regarding vehicle lights in different weather conditions. On rainy days, drivers often turn on their vehicle's parking lights to ensure their safety. This is less common on sunny days. If these details are analyzed meticulously from the outset when building the driver preference database, the database will have a more detailed record of drivers' actual preferences, facilitating more refined use of historical data in subsequent applications.

[0087] Of course, this embodiment only considers two prerequisites. Following this line of thought, there can be other implementation schemes with more reference data sources, which also fall within the scope of the technical solutions protected by this invention.

[0088] Figure 4 This is a flowchart of the database establishment operation in the intelligent voice-controlled vehicle lighting method provided in one or more embodiments of the present invention. See also Figure 4 Based on historical voice commands and corresponding lighting parameters, a driver preference database is established, including:

[0089] S41, statistically analyzes the final executed lighting parameters under the same voice command conditions in the historical records.

[0090] S42 sorts the frequency of occurrence of all combinations of lighting parameters under the same voice command conditions.

[0091] S43. The top-ranked combinations of lighting parameters in the sorting order are written into the driver preference database.

[0092] In the foregoing embodiments of the present invention, each data entry actually written to the driver preference database contains only one lighting parameter. For example, if the lighting parameter includes headlight brightness, then each data entry contains only that one lighting parameter.

[0093] However, this is severely disconnected from the actual situation of lighting control. In real-world lighting control scenarios, it is usually necessary not only to know the actual brightness of the vehicle when it is turned on, but also whether the headlights, taillights, or both are on.

[0094] In more sophisticated scenarios, it is also necessary to know whether the lights need to flash, the frequency of flashing, and whether the brightness of the vehicle's lights changes over time during the flashing process, among other things.

[0095] Therefore, for such a need, it is clearly insufficient to record only one lighting parameter in each data entry.

[0096] For the reasons mentioned above, in the process of establishing the driver preference database, instead of recording just one lighting parameter in each data entry, multiple data fields are opened to record a combination of lighting parameters.

[0097] After switching to recording combinations of lighting parameters, the actual control effect, i.e., the lighting parameters, will show multiple parameters working together. This significantly enriches the control effects.

[0098] After recording data entries using combinations of lighting parameters, the same lighting parameter may correspond to several different combinations of lighting parameters in the database. In this case, the statistics and sorting of different data entries will no longer be based on a single lighting parameter, but rather on the combinations that appear in the recorded lighting parameter sets.

[0099] For example, suppose the actual recorded lighting parameter combinations use two data items: headlight brightness and headlight type. During data statistics, data items with the same headlight brightness but different headlight types should be counted as two separate data items, with their occurrence counts calculated separately. They should not be included in the overall count for the same headlight brightness.

[0100] Because more factors are considered during sorting and statistical analysis, data sparsity may occur under each data item. This could significantly reduce the amount of useful data actually recorded in the database. To avoid data sparsity caused by the increase in data items, a scheme of dynamically adjusting the recording threshold is adopted during the truncation of sorting results.

[0101] In other words, previously only the light parameter combination that appeared first in the sorting was recorded, but now it can record the top three light parameter combinations during the sorting process. In cases where data is severely insufficient, this threshold can be adjusted to the top five.

[0102] Figure 5 This is a flowchart of the fusion decision-making operation in the intelligent voice-controlled vehicle lighting method provided in one or more embodiments of the present invention. See also Figure 5 When a voice command is input, the system fuses the received voice command with the current scene parameters to make a decision, and avoids decisions that pose safety risks. This includes the following steps:

[0103] S51 categorizes the current scene parameters into typical scene types.

[0104] S52, obtain the list of dangerous actions corresponding to typical scenario types.

[0105] S53 uses a list of dangerous actions to filter the actions corresponding to voice commands.

[0106] The main purpose of integrated decision-making is to avoid potential security risks.

[0107] In this embodiment, a list of dangerous actions is used to avoid safety hazards.

[0108] In simple terms, it involves categorizing different combinations of scenario parameters, resulting in several typical scenario types. For each typical scenario type, there is a corresponding list of dangerous actions. When the action corresponding to a voice command exists in the list of dangerous actions for the current scenario, the execution of that action must be stopped and cannot be carried out.

[0109] The classification mentioned in this embodiment can be either the action of categorizing or the action of clustering.

[0110] When the basic data is complete, classification can be performed. However, when the basic data is not complete and further observation of data features is required, clustering can be performed.

[0111] From the perspective of practical computing power applications, clustering can be performed when computing resources are sufficient. However, clustering should be avoided when computing resources are limited.

[0112] It should be understood that certain actions are dangerous in every typical scenario. For example, leaving a vehicle outdoors in hot, sunny weather for an extended period is unsafe. Driving in heavy snow without snow chains is dangerous. Frequent lane changes on highways are dangerous. Using bright lights inside the vehicle at night is dangerous.

[0113] In this embodiment, for each typical scenario type, the specific actions most likely to lead to safety accidents in that scenario are summarized. The result of this summary is a list of dangerous actions that drivers should not take in that scenario.

[0114] Upon receiving a voice command, the system first compares the target action of the command with the list of dangerous actions corresponding to the current scene. The action is only executed if the target action does not appear on the list. If the target action is found on the list and a corresponding data item exists, the execution of that action is stopped.

[0115] The above technical solutions can effectively prevent dangerous actions and avoid potential safety hazards.

[0116] Figure 6 This is a flowchart of the fusion decision-making operation in the intelligent voice-controlled vehicle lighting method provided in one or more embodiments of the present invention. See also Figure 6 When a voice command is input, the system fuses the received voice command with the current scene parameters to make a decision, and avoids decisions that pose safety risks. This includes the following steps:

[0117] S61 categorizes the current scene parameters into typical scene types.

[0118] S62, obtain the list of dangerous actions corresponding to typical scenario types.

[0119] S63 uses a list of dangerous actions to filter the actions corresponding to voice commands.

[0120] S64, retrieve the actions associated with the voice command in a typical scenario.

[0121] S65 executes actions corresponding to or associated with voice commands.

[0122] It should be understood that the user's voice command points to a list of dangerous actions in the current scenario, but the user's original intention in issuing this voice command is usually not to perform any dangerous actions.

[0123] In this situation, target actions in the list of dangerous actions usually have some related actions. Related actions can be actions with similar functions to the target action, or actions that are closely related to the target action in terms of operational logic.

[0124] For example, the action of turning on the air conditioning in the cabin to cool down could be similar to turning on the air conditioning to dehumidify.

[0125] For example, turning on the ambient lighting in the cabin could be closely related to the action of turning on the music in the cabin.

[0126] If an action already appears on the list of dangerous actions in the current scenario, it can be replaced with an action similar in function to the target action, or an action whose operational logic is closely related to the dangerous action. In this way, user needs can be partially met while mitigating safety risks.

[0127] Figure 7 This is a flowchart of the fusion decision-making operation in the intelligent voice-controlled vehicle lighting method provided in one or more embodiments of the present invention. See also Figure 7 When a voice command is input, the system fuses the received voice command with the current scene parameters to make a decision, and avoids decisions that pose safety risks. This includes the following steps:

[0128] S71, based on the current scenario parameters, queries the driver preference database.

[0129] S72 executes the action associated with the input voice command, which is retrieved from the driver preference database.

[0130] Unlike the aforementioned embodiments of the present invention, the fusion decision-making process in this embodiment is a process of directly applying the control actions stored in the established preference database.

[0131] It can be applied directly without the need for complex fusion calculations or intricate decision-making processes. The process is simple and meets the actual needs of drivers, making it straightforward and easy to understand.

[0132] Figure 8 This is a structural diagram of an intelligent voice-activated vehicle light control device provided in one or more embodiments of the present invention. See also... Figure 8 The intelligent voice-controlled vehicle lighting device includes:

[0133] The parameter acquisition module 81 is used to continuously and synchronously acquire scene parameters fed back from the vehicle.

[0134] The recording module 82 is used to record each voice command issued by the driver and the actual lighting parameters that are ultimately executed and optimized by context fusion.

[0135] The database creation module 83 is used to create a driver preference database based on historical voice commands and corresponding lighting parameters.

[0136] The fusion decision module 84 is used to fuse the obtained voice command with the current scene parameters when there is a voice command input, and to avoid decisions that pose safety risks.

[0137] It is worth noting that although only some basic functional modules are disclosed in the embodiments of this invention, it does not mean that the composition of this system is limited to the above-mentioned basic functional modules. On the contrary, what this embodiment intends to express is that, based on the above-mentioned basic functional modules, those skilled in the art can arbitrarily add one or more functional modules in combination with existing technology to form an infinite number of embodiments or technical solutions. That is to say, this system is open rather than closed. The fact that this embodiment only discloses a few basic functional modules should not be considered as the scope of protection of the claims of this invention being limited to the disclosed basic functional modules. At the same time, for the convenience of description, the above device is described separately according to its functions as various units and modules. Of course, in implementing this invention, the functions of each unit and module can be implemented in one or more software and / or hardware.

[0138] like Figure 9 As shown, the present invention also provides a vehicle infotainment system, including: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the intelligent voice headlight control method.

[0139] Figure 9 This is a structural schematic diagram of a vehicle-mounted infotainment system provided in an embodiment of the present invention. Figure 9 The structure shown in this embodiment of the invention includes one or more processors 910 and a memory 920; the processors 910 in the vehicle infotainment system can be one or more. Figure 9 Taking a processor 910 as an example; a memory 920 is used to store one or more programs; the one or more programs are executed by the one or more processors 910, so that the one or more processors 910 implement the intelligent voice vehicle light control method as described in any one of the embodiments of the present invention.

[0140] The vehicle infotainment system may also include an input device 930 and an output device 940.

[0141] The processor 910, memory 920, input device 930, and output device 940 in the vehicle's infotainment system can be connected via a bus or other means. Figure 9 Taking the example of a connection between China and Israel via a bus.

[0142] The memory 920 in the vehicle infotainment system serves as a computer-readable storage medium, capable of storing one or more programs. These programs can be software programs, computer-executable programs, or modules, such as the program instructions / modules corresponding to the intelligent voice-activated headlight control method provided in this embodiment. The processor 910 executes various functional applications and data processing of the vehicle infotainment system by running the software programs, instructions, and modules stored in the memory 920, thereby implementing the intelligent voice-activated headlight control method described in the above embodiment.

[0143] The memory 920 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the vehicle's infotainment system. Furthermore, the memory 920 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 920 may further include memory remotely located relative to the processor 910, which can be connected to the device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0144] Input device 930 can be used to receive input digital or character information, and to generate key signal inputs related to user settings and function control of the vehicle system. Output device 940 may include display devices such as a display screen.

[0145] The present invention also provides a computer-readable storage medium storing a computer program executable by a vehicle-mounted system, wherein when the computer program is run on the vehicle-mounted system, the vehicle-mounted system performs the steps of an intelligent voice-controlled headlight method.

[0146] Specifically, the computer storage medium in this embodiment of the invention can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. For example, a computer-readable storage medium can be—but is not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0147] The present invention also provides a vehicle equipped with the intelligent voice headlight control device described above.

[0148] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A smart voice car light control method, characterized in that, The intelligent voice vehicle lamp control method comprises: Continuously and synchronously collecting scene parameters from vehicle feedback; Recording each time the driver actively issues a voice instruction and the actual light parameter finally executed after situation fusion optimization; Establishing a driver preference database according to the historical recorded voice instructions and the corresponding light parameters; When a voice instruction is input, the obtained voice instruction is fused with the current scene parameter for decision-making, and decisions with safety hazards are avoided.

2. The method of claim 1, wherein, Establishing a driver preference database according to the historical recorded voice instructions and the corresponding light parameters comprises: Counting the finally executed light parameters under the same voice instruction condition in the historical record; Recording the light parameter with the highest occurrence frequency under the same voice instruction condition in the driver preference database.

3. The method of claim 2, wherein, Counting the finally executed light parameters under the same voice instruction condition in the historical record comprises: Counting the finally executed light parameters under the same voice instruction and the same scene parameter condition in the historical record.

4. The method of claim 2, wherein, Recording the light parameter with the highest occurrence frequency under the same voice instruction condition in the driver preference database comprises: Sorting the occurrence frequencies of all light parameter combinations under the same voice instruction condition; Writing the top several light parameter combinations in the sequence into the driver preference database.

5. The method of claim 1, wherein, When a voice instruction is input, the obtained voice instruction is fused with the current scene parameter for decision-making, and decisions with safety hazards are avoided, which comprises: Classifying the current scene parameter into a typical scene type; Obtaining a dangerous action list corresponding to the typical scene type; Filtering the action corresponding to the voice instruction by using the dangerous action list.

6. The method of claim 5, wherein, When a voice instruction is input, the obtained voice instruction is fused with the current scene parameter for decision-making, and decisions with safety hazards are avoided, which further comprises: Obtaining an associated action of the action corresponding to the voice instruction under the typical scene type; Executing the associated action of the action corresponding to the voice instruction.

7. The method of claim 2, wherein, When a voice instruction is input, the obtained voice instruction is fused with the current scene parameter for decision-making, and decisions with safety hazards are avoided, which comprises: Querying the driver preference database according to the current scene parameter; Executing the action associated with the input voice instruction in the driver preference database.

8. An intelligent voice car light control device, characterized by, The intelligent voice vehicle lamp control device comprises: A parameter collection module for continuously and synchronously collecting scene parameters from vehicle feedback; A recording module for recording each time the driver actively issues a voice instruction and the actual light parameter finally executed after situation fusion optimization; A database establishment module for establishing a driver preference database according to the historical recorded voice instructions and the corresponding light parameters; A fusion decision module for fusing the obtained voice instruction with the current scene parameter for decision-making when a voice instruction is input, and avoiding decisions with safety hazards.

9. A car kit, characterized by The car machine comprises a processor and a memory, the memory stores a computer program, the computer program is loaded and executed by the processor to realize the intelligent voice car light control method as claimed in any one of claims 1 to 7.

10. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to realize the intelligent voice car light control method as claimed in any one of claims 1 to 7.

Images