Rearview mirror control system and method based on large language model and medium

Abstract

The invention discloses a rearview mirror control system and method based on a large language model and a medium. The method comprises the following steps: acquiring driver information, sensing a driving environment and acquiring adjustment input data through a vehicle-mounted sensor module; according to the adjustment input data, a rearview mirror adjustment instruction is generated through the large language model module; the vehicle machine system module analyzes the rearview mirror adjusting instruction and controls a rearview mirror driving system to execute corresponding actions; and the rearview mirror driving module receives the control signal sent by the vehicle machine system module and adjusts the rearview mirror. According to the method, the driver information and the environment information are integrated through the large language model, the corresponding adjustment instruction is generated through reasoning, the rearview mirror is controlled to execute corresponding actions through the CAN signal after the instruction is analyzed by the vehicle machine system, and the driving experience and safety of the driver are improved.

Description

Technical Field

[0001] This invention relates to the field of automotive control, and more specifically, to a rearview mirror control system, method, and medium based on a large language model. Background Technology

[0002] With the continuous advancement of in-vehicle technology, in-vehicle intelligent systems have become an important component of modern automobiles. Existing rearview mirror adjustment methods are mostly traditional physical controls, requiring drivers to manually adjust the mirrors using buttons or knobs. However, traditional rearview mirror adjustment methods have some shortcomings: 1. Cumbersome manual operation: Drivers need to manually adjust the position of the rearview mirrors, which is inconvenient, especially when switching between different drivers or passengers.

[0003] 2. Lack of personalization: The existing system cannot automatically adjust the rearview mirror according to the driver's personalized needs, height, seat position and other parameters, and cannot provide a more accurate and comfortable view.

[0004] 3. Inability to adapt to environmental changes in real time: The existing system fails to take into account the impact of environmental changes during driving, such as the different visibility requirements under different road and weather conditions.

[0005] 4. Passive adjustment: In existing technologies, the adjustment of rearview mirrors is often a passive process, and it is impossible to make real-time adjustments according to the driver's individual needs or driving environment. Especially in multi-driver vehicles, the rearview mirrors need to be manually adjusted every time the driver switches.

[0006] Therefore, how to utilize modern artificial intelligence technology, especially large language model (LLM), combined with vehicle sensor information to achieve automated, proactive, and intelligent rearview mirror control has become a major requirement for improving driver comfort and safety. It is necessary to develop a rearview mirror control system, method, and medium based on large language model.

[0007] The information disclosed in the background section of this invention is intended only to enhance the understanding of the general background of this invention, and should not be construed as an admission or in any way implying that such information constitutes prior art known to those skilled in the art. Summary of the Invention

[0008] This invention proposes a rearview mirror control system, method, and medium based on a large language model. It can automatically calculate and adjust the rearview mirror angle by combining the driver's height, seat position, personal habits, and real-time driving environment (such as slope), achieving intelligent and personalized rearview mirror adjustment. By integrating driver and environmental information through a large language model and inferring corresponding adjustment commands, the vehicle's infotainment system parses the commands and controls the rearview mirror to perform the corresponding actions via CAN signals, improving the driver's driving experience and safety.

[0009] In a first aspect, embodiments of this disclosure provide a rearview mirror control system based on a large language model, comprising: The vehicle-mounted sensor module is used to acquire and adjust input data; The large language model module generates rearview mirror adjustment commands based on the adjustment input data; The vehicle infotainment system module is used to parse the rearview mirror adjustment command and control the rearview mirror drive system to perform the corresponding actions. The rearview mirror drive module receives control signals from the vehicle infotainment system module and adjusts the position and angle of the rearview mirror.

[0010] Preferably, the vehicle-mounted sensor module includes a seat position sensor, a driver identification module, a door status detection sensor, and a slope detection sensor.

[0011] Preferably, the adjustment input data includes the driver's height, seat position, door status, current driving slope, and road curvature.

[0012] Secondly, embodiments of this disclosure also provide a rearview mirror control method based on a large language model, including: The vehicle-mounted sensor module acquires driver information, senses the driving environment, and obtains adjustment input data. Based on the adjustment input data, a rearview mirror adjustment command is generated through the large language model module; The vehicle infotainment system module parses the rearview mirror adjustment command and controls the rearview mirror drive system to perform the corresponding action; The rearview mirror drive module receives control signals from the vehicle infotainment system module and adjusts the rearview mirror.

[0013] Preferably, the adjustment input data includes the driver's height, seat position, door status, current driving slope, and road curvature.

[0014] Preferably, the rearview mirror adjustment command includes adjusting the angle and position.

[0015] Preferably, generating rearview mirror adjustment instructions through the large language model module based on the adjustment input data includes: Based on the adjustment input data, determine whether there is data in the historical data that is the same as or similar to the adjustment input data. If so, extract the corresponding rearview mirror adjustment command. If not, the large speech model combines the input data to perform reasoning analysis and generate the corresponding rearview mirror adjustment command.

[0016] Preferably, the large language model module generates the rearview mirror adjustment command directly after the user gets into the vehicle; or

[0017] The large language model module generates the rearview mirror adjustment command based on the user's voice trigger.

[0018] Preferably, it further includes: After the rearview mirror is adjusted, the on-board sensors monitor the user's field of vision. If the user is not satisfied, the mirror can be adjusted again via voice command.

[0019] Thirdly, embodiments of this disclosure also provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the rearview mirror control method based on a large language model.

[0020] Its beneficial effects are as follows: 1. Intelligent reasoning and personalized adjustment combined with large language model: Utilize large language model (LLM) for deep reasoning analysis, and combine multiple factors such as driver's height, seat position, height, personal habits and real-time driving environment to achieve highly personalized automatic adjustment of rearview mirror.

[0021] 2. Real-time driving environment perception: It not only considers the driver's personalized needs, but also combines real-time driving environment factors (such as slope, lighting, etc.), so that the rearview mirror adjustment can be dynamically optimized according to the changes in the driving scenario, providing the best driving vision.

[0022] 3. Active and passive adjustment: The system supports active adjustment of the rearview mirror based on the collected information, and also supports fine-tuning via voice commands to achieve real-time adjustment of the rearview mirror. The system can dynamically optimize the rearview mirror angle according to changes in the environment.

[0023] The methods and apparatus of the present invention have other features and advantages that will be apparent from or will be set forth in detail in the accompanying drawings and following detailed description, which together serve to explain the particular principles of the invention. Attached Figure Description

[0024] The above and other objects, features and advantages of the present invention will become more apparent from the more detailed description of exemplary embodiments of the invention in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same parts.

[0025] Figure 1 A flowchart illustrating the steps of a rearview mirror control method based on a large language model according to an embodiment of the present invention is shown. Detailed Implementation

[0026] Preferred embodiments of the invention will now be described in more detail. While preferred embodiments of the invention are described below, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0027] To facilitate understanding of the solutions and effects of the embodiments of the present invention, three specific application examples are given below. Those skilled in the art should understand that these examples are merely for the purpose of understanding the present invention, and any specific details therein are not intended to limit the present invention in any way.

[0028] Example 1

[0029] A rearview mirror control system based on a large language model includes: The vehicle-mounted sensor module is used to acquire and adjust input data; The large language model module generates rearview mirror adjustment commands based on the adjustment input data; The vehicle infotainment system module is used to parse rearview mirror adjustment commands and control the rearview mirror drive system to perform corresponding actions. The rearview mirror drive module receives control signals from the vehicle's infotainment system module and adjusts the position and angle of the rearview mirror.

[0030] In one example, the vehicle sensor module includes a seat position sensor, a driver identification module, a door status detection sensor, and a slope detection sensor.

[0031] In one example, the input data to be adjusted includes the driver's height, seat position, door status, current driving slope, and road curvature.

[0032] Specifically, the rearview mirror control system based on a large language model of the present invention includes the following modules: The vehicle-mounted sensor module includes a seat position sensor, a driver identification module, a door status detection sensor, and a slope detection sensor. These sensors acquire information such as the driver's height, seat position, door status, and the current driving environment (e.g., slope, road curvature).

[0033] Large Language Model (LLM) module: Combines personal information and real-time data provided by sensors to infer and generate rearview mirror adjustment instructions.

[0034] Vehicle infotainment system module: Parses the JSON instructions generated by the LLM and controls the rearview mirror drive system to perform corresponding actions.

[0035] Rearview mirror drive module: Receives control signals from the vehicle's infotainment system via the CAN bus to adjust the position and angle of the rearview mirror.

[0036] Example 2

[0037] Figure 1 A flowchart illustrating the steps of a rearview mirror control method based on a large language model according to an embodiment of the present invention is shown.

[0038] like Figure 1 As shown, the rearview mirror control method based on a large language model includes: Step 101: Obtain driver information, perceive the driving environment, and acquire adjustment input data through the vehicle sensor module; Step 102: Based on the adjustment input data, generate rearview mirror adjustment instructions through the large language model module; Step 103: The vehicle system module parses the rearview mirror adjustment command and controls the rearview mirror drive system to perform the corresponding action; Step 104: The rearview mirror drive module receives the control signal from the vehicle infotainment system module and adjusts the rearview mirror.

[0039] In one example, the input data to be adjusted includes the driver's height, seat position, door status, current driving slope, and road curvature.

[0040] In one example, the rearview mirror adjustment command includes adjusting the angle and position.

[0041] In one example, based on the adjustment input data, the large language model module generates rearview mirror adjustment instructions, including: Based on the adjustment input data, determine whether there is data in the historical data that is the same as or similar to the adjustment input data. If so, extract the corresponding rearview mirror adjustment command. If not, the large speech model combines the input data to perform reasoning analysis and generate the corresponding rearview mirror adjustment command.

[0042] In one example, the large language model module recognizes the user getting into the car and directly generates a rearview mirror adjustment command; or

[0043] The large language model module generates rearview mirror adjustment commands based on user voice triggers.

[0044] In one example, it also includes: After the rearview mirror is adjusted, the vehicle's sensors monitor the user's field of vision. If the user is not satisfied, the mirror can be adjusted again via voice command.

[0045] Specifically, when the car door closes, the onboard sensor module identifies the driver through the identity recognition module and obtains personalized information such as the driver's height and seat height by combining it with the seat position sensor. At this time, the onboard system initializes the driver's personal preferences and needs.

[0046] The onboard sensor module simultaneously detects information about the current driving environment, including road gradient, vehicle speed, and external light intensity. This environmental data is used to adjust the rearview mirror angle to ensure the driver has the best field of vision in different driving situations.

[0047] After the driver gets into the vehicle, the system can either proactively execute commands or passively wait for the driver to issue voice commands (e.g., "Adjust the rearview mirror" or "Please raise the rearview mirror angle"). Upon receiving the command, the large speech model combines the driver's height, seat position, personal habits, and real-time driving environment data to generate the corresponding rearview mirror adjustment command through inference analysis. Based on the adjustment input data, it determines whether there is data in the historical data that is the same as or similar to the adjustment input data. If so, the corresponding rearview mirror adjustment command is extracted; otherwise, the large speech model combines the input data to generate the corresponding rearview mirror adjustment command through inference analysis.

[0048] Computer vision technology can also be used to identify the driver's facial features or posture through a camera to deduce the most suitable rearview mirror position; different driver profiles can also be preset, and the system can automatically adjust the rearview mirror by selecting the corresponding profile.

[0049] The large language model generates JSON-formatted instructions from the inference results (e.g., {"action": "adjust_mirror", "angle": 45, "position": "higher"}), and sends them to the rearview mirror drive module via the vehicle's infotainment system. These instructions contain key information such as the adjustment angle and position.

[0050] The rearview mirror drive module receives control signals from the vehicle system via the CAN bus, executes corresponding rearview mirror adjustment actions, and adjusts the rearview mirror angle in real time to ensure that the driver has the best field of vision.

[0051] After adjustment, the system monitors the driver's field of vision using onboard sensors. If the driver is not satisfied with the viewing angle, they can readjust it via voice command. The system dynamically optimizes the rearview mirror angle based on feedback to suit the driver's individual needs.

[0052] This method utilizes intelligent analysis of a large language model, enabling the system to automatically adjust the rearview mirrors based on the driver's habits and body type, providing a personalized driving experience. Drivers no longer need to manually adjust the mirrors; the large language model integrates information and automatically adjusts them, reducing the driver's workload and improving driving safety. Through real-time perception of environmental data such as slope and lighting, the rearview mirrors can automatically adjust in different driving environments to provide optimal visibility and ensure driving safety. Through interaction with the large language model, drivers can directly provide feedback on the rearview mirror adjustments, and the system can self-optimize based on this feedback.

[0053] Example 3

[0054] This disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the rearview mirror control method based on a large language model.

[0055] A computer-readable storage medium according to embodiments of the present disclosure stores non-transitory computer-readable instructions. When these non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the methods described in the foregoing embodiments of the present disclosure are performed.

[0056] The aforementioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or portable hard drive), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).

[0057] Those skilled in the art should understand that the above description of the embodiments of the present invention is only intended to illustrate the beneficial effects of the embodiments of the present invention, and is not intended to limit the embodiments of the present invention to any of the examples given.

[0058] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A rearview mirror control system based on a large language model, characterized in that, include: The vehicle-mounted sensor module is used to acquire and adjust input data; The large language model module generates rearview mirror adjustment commands based on the adjustment input data; The vehicle infotainment system module is used to parse the rearview mirror adjustment command and control the rearview mirror drive system to perform the corresponding actions. The rearview mirror drive module receives control signals from the vehicle infotainment system module and adjusts the position and angle of the rearview mirror.

2. The rearview mirror control system based on a large language model according to claim 1, wherein, The vehicle-mounted sensor module includes a seat position sensor, a driver identification module, a door status detection sensor, and a slope detection sensor.

3. The rearview mirror control system based on a large language model according to claim 1, wherein, The adjustment input data includes the driver's height, seat position, door status, current driving slope, and road curvature.

4. A rearview mirror control method based on a large language model, utilizing the rearview mirror control system based on a large language model according to any one of claims 1-3, characterized in that, include: The vehicle-mounted sensor module acquires driver information, senses the driving environment, and obtains adjustment input data. Based on the adjustment input data, a rearview mirror adjustment command is generated through the large language model module; The vehicle infotainment system module parses the rearview mirror adjustment command and controls the rearview mirror drive system to perform the corresponding action; The rearview mirror drive module receives control signals from the vehicle infotainment system module and adjusts the rearview mirror.

5. The rearview mirror control method based on a large language model according to claim 4, wherein, The adjustment input data includes the driver's height, seat position, door status, current driving slope, and road curvature.

6. The rearview mirror control method based on a large language model according to claim 5, wherein, The rearview mirror adjustment commands include adjusting the angle and position.

7. The rearview mirror control method based on a large language model according to claim 6, wherein, Based on the adjustment input data, the rearview mirror adjustment instructions generated by the large language model module include: Based on the adjustment input data, determine whether there is data in the historical data that is the same as or similar to the adjustment input data. If so, extract the corresponding rearview mirror adjustment command. If not, the large speech model combines the input data to perform reasoning analysis and generate the corresponding rearview mirror adjustment command.

8. The rearview mirror control method based on a large language model according to claim 4, wherein, The large language model module recognizes the user getting into the vehicle and directly generates the rearview mirror adjustment command; or The large language model module generates the rearview mirror adjustment command based on the user's voice trigger.

9. The rearview mirror control method based on a large language model according to claim 4, wherein, Also includes: After the rearview mirror is adjusted, the on-board sensors monitor the user's field of vision. If the user is not satisfied, the mirror can be adjusted again via voice command.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the rearview mirror control method based on a large language model as described in any one of claims 4-9.

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