Multi-source information fusion automobile light personalized interaction control system and method

Abstract

The invention provides a multi-source information fusion automobile light personalized interaction control system and method, and relates to the technical field of automobile intelligent light control. The system comprises a multi-source sensing module, a data preprocessing module, a data analysis processing module and a vehicle lamp driving module. The multi-source sensing module collects multi-source heterogeneous data, after the multi-source heterogeneous data is processed by the data preprocessing module, fusion analysis and intelligent decision are carried out by the data analysis processing module, a control instruction is generated, and finally, the instruction is executed by the vehicle lamp driving module and a state is fed back. According to the invention, the defects of the existing automobile light control technology in the aspects of intelligence, situational and individuation are overcome, the efficient, intelligent and individualized control of the automobile light is realized, and the driving safety, comfort and entertainment are improved.

Description

Technical Field

[0001] This invention relates to the field of automotive intelligent lighting control technology, specifically to a personalized interactive control system and method for automotive lighting that integrates multi-source information. Background Technology

[0002] In existing technologies, automotive lighting control primarily focuses on the implementation and control of basic functions of legally mandated lighting fixtures, such as high beams, low beams, position lights, turn signals, and fog lights. Control methods mainly include physical interaction, such as direct operation via hardware switches like steering wheel levers and center console buttons; and basic intelligent interaction, such as using voice commands or mobile apps to switch specific lights on / off or in different modes. The level of intelligent interaction in these latter two categories is relatively low.

[0003] With the rapid advancement of automotive intelligence, automotive lighting, especially high-pixel, high-resolution lights such as DLP digital light processing headlights, ADB adaptive high beams, and ISD intelligent interactive lights, has far surpassed the traditional scope of lighting and signal warning, gradually evolving into an important medium for in-vehicle information interaction and emotional expression. However, existing lighting control technologies have the following significant limitations, making it difficult to meet the demands of intelligent development: (1) Limited control objects: Existing intelligent control methods (voice, mobile terminal APP, etc.) are mainly adapted to legally mandated lighting fixtures with clear function definitions and simple state switching. They are not well supported by emerging high-pixel intelligent interactive lighting fixtures that can present rich dynamic images and complex light effects, and lack effective control interfaces and logic.

[0004] (2) The interaction dimension is shallow: The existing control methods (physical switch, simple voice command, APP switch, vehicle screen soft switch) are essentially mapping the traditional physical switch to the digital interface or voice channel. The interaction dimension is single (on / off, mode switching), and it cannot achieve the fine, parameterized or scenario-based flexible control of complex light effects (such as dynamic patterns, brightness gradient, color change, precise area control).

[0005] (3) Lack of intelligence and context awareness: Existing control technologies generally lack deep intelligent decision-making capabilities and cannot automatically adapt and dynamically adjust lighting effects according to real-time driving environment (such as weather, road conditions, surrounding vehicle and pedestrian status), vehicle status (such as speed, steering, driving mode) or user personalized needs and intentions.

[0006] (4) It cannot support natural, efficient, and context-related light interaction based on multimodal input, such as combining speech semantics, gestures, biometrics, navigation information, etc.

[0007] (5) Disjointed user experience and low efficiency: Relying on physical buttons or simple voice commands to control complex lighting effects, the operation steps are cumbersome and the learning cost is high. Furthermore, it cannot achieve a real-time, smooth, and personalized lighting interaction experience, which is out of step with the development trend of intelligent cockpits in vehicles.

[0008] (6) Unreleased control potential: Current advanced high-pixel lighting fixtures (DLP, ADB, ISD, etc.) have powerful display and interaction potential, but due to outdated control architecture and methods, their rich visual expressiveness and information carrying capacity have not been fully explored and effectively utilized.

[0009] The above-mentioned problems urgently need to be solved. Summary of the Invention

[0010] The purpose of this invention is to overcome at least one technical problem existing in the prior art and to provide a multi-source information fusion-based personalized interactive control system and method for automotive lighting.

[0011] On one hand, embodiments of the present invention provide a multi-source information fusion-based personalized interactive control system for automotive lighting. The control system includes: a multi-source perception module, a data preprocessing module, a data parsing and processing module, and a headlight driving module. The multi-source perception module is used to collect voice commands, image data, and in-vehicle application data in real time. The data preprocessing module is used to preprocess the voice commands, converting the voice signals into data information; preprocess the image data to identify user action information and road environment information of the vehicle; and preprocess the in-vehicle application data to obtain one or a combination of dynamic driving data, environmental parameter data, entertainment system data, navigation data, calendar events, and driver behavior data. The data parsing and processing module integrates a rule base, a scene arbitrator, and a pattern conflict resolution mechanism. The system includes a processor; a rule base storing security policy rules, environment adaptation rules, entertainment control rules, and interactive scene rules; a scene arbitrator receiving data processed by the data preprocessing module and determining the actual needs of the current driver and passengers, the actual environment of the vehicle, and the real-time scene inside the vehicle based on the security policy rules, environment adaptation rules, entertainment control rules, and interactive scene rules, generating corresponding lighting control instructions, including lighting type and control parameters; a mode conflict processor receiving data processed by the data preprocessing module and controlling the execution priority of each rule in the scene arbitrator based on pre-stored conflict handling rules; and a headlight drive module driving the LED array to output corresponding light effects based on the lighting control instructions generated by the scene arbitrator.

[0012] Furthermore, the multi-source perception module integrates a sound collector, an image collector, and an in-vehicle application data collector. The sound collector employs a ring-distributed six-microphone array, using beamforming technology to achieve sound source localization, distinguishing voice commands from the driver, front passenger, and other passengers. The image collector includes an in-vehicle camera module and an external camera module. The external camera module integrates an embedded target detection algorithm to monitor the vehicle's current environmental road conditions. The in-vehicle camera module uses near-infrared technology to monitor the driver's eyelid opening and head posture in real time, detecting driver fatigue. The in-vehicle application data collector acquires heterogeneous data from multiple subsystems of the vehicle's electronic architecture in real time and performs preliminary feature extraction.

[0013] Furthermore, the data preprocessing module integrates a voice preprocessing module, an image preprocessing module, and an in-vehicle application data preprocessing module. The voice preprocessing module performs noise reduction, feature extraction, and voice recognition processing on the collected voice commands, converting the voice signals into data information that a computer can recognize and process. The image preprocessing module performs filtering, enhancement, and target detection processing on the image data, identifying key information in the image, including driver action information and / or road environment information where the vehicle is located. The in-vehicle application data preprocessing module performs preprocessing on the heterogeneous data collected from various vehicle sensors and vehicle infotainment systems to extract feature data, and performs normalization and timestamp alignment processing on the feature data.

[0014] Furthermore, the speech preprocessing module is used to remove noise contained in the speech commands using an adaptive filtering algorithm; to extract phonemes, tones, and / or energy features from the speech commands using a speech feature extraction algorithm; and to convert the speech signal into data information using a deep learning speech recognition model. The image preprocessing module is used to remove noise from the image using a filtering algorithm to improve image clarity; to enhance the contrast and brightness of the image using adaptive enhancement technology to highlight key features in the image; and to detect and identify people, objects, and the environment in the image using a deep learning-based object detection algorithm. The vehicle application data preprocessing module is used to detect abrupt changes in data and remove outliers using a sliding window algorithm; to unify data from different buses to the same time base using a clock synchronization protocol; to extract key features from the synchronized data from different buses; and to normalize the key features and generate structured data.

[0015] Furthermore, the key features include driving status, control signals and / or energy management data in dynamic driving data, light intensity, meteorological parameters and / or visual aid data in environmental parameters, music tempo value (BPM), audio spectrum characteristics and / or media metadata in entertainment system data, route planning, tunnel / bridge location and / or real-time traffic data in navigation data, holiday data in calendar events, and operating habits, interaction preferences and / or biometric data in driver behavior data.

[0016] Furthermore, the safety policy rules include visibility classification response rules and collision avoidance intervention rules. The visibility classification response rules store the mapping relationship between visibility in environmental parameter data and lighting control commands. The collision avoidance intervention rules store the mapping relationship between the distance to the preceding vehicle and the cut-in angular velocity collected by the image acquisition device and the lighting control commands. The environmental adaptation rules include road condition lighting rules and weather response rules. The road condition lighting rules store the mapping relationship between the road condition type collected by the image acquisition device and the lighting control commands. The weather response rules store the mapping relationship between the weather conditions collected by the in-vehicle application data acquisition device and the lighting control commands. The entertainment control rules include holiday theme linkage rules and audio-visual linkage rules. The holiday theme linkage rules store the mapping relationship between calendar events collected by the in-vehicle application data acquisition device and the lighting control commands. The audio-visual linkage rules store the mapping relationship between music features collected by the in-vehicle application data acquisition device and the lighting control commands. The interactive scene rules store the mapping relationship between voice interaction commands collected by the sound acquisition device and the lighting control commands.

[0017] Furthermore, the conflict handling rules include a higher control priority for security policy rules than for environment adaptation rules, which in turn have a higher control priority than for interaction scenario rules, which in turn have a higher control priority than for entertainment control rules.

[0018] Furthermore, the data parsing and processing module also integrates a personalized preference engine, which is used to introduce driver preference configuration files, store the driver's user ID and preference information, as well as the mapping relationship between the preference information and the lighting control commands, and optimize the lighting control commands based on the stored mapping relationship between the preference information and the lighting control commands during the scene arbitrator's control of the lights.

[0019] Furthermore, the vehicle lighting drive module includes an LED array, a spectral modulation unit, and a closed-loop feedback component; the closed-loop feedback component collects real-time light brightness, temperature, and fault information, and sends it to the data parsing and processing module for display.

[0020] Secondly, embodiments of the present invention provide a multi-source information fusion-based personalized interactive control method for automotive lighting. The control method employs the aforementioned multi-source information fusion-based personalized interactive control system for automotive lighting. The method includes: Step S1, real-time acquisition of voice commands, image data, and in-vehicle application data via a multi-source sensing module; Step S2, preprocessing the voice commands via a data preprocessing module to convert the voice signal into data information; Step S3, preprocessing the image data via a data preprocessing module to identify user action information and road environment information of the vehicle; Step S4, preprocessing the in-vehicle application data via a data preprocessing module to obtain dynamic driving data, environmental parameter data, entertainment system data, navigation data, and calendar events. The system receives data from the preprocessing module, including one or a combination of driver behavior data and scene arbitrator data. Step S5: The scene arbitrator receives the processed data from the preprocessing module and, based on pre-stored safety policy rules, environmental adaptation rules, entertainment control rules, and interactive scene rules, determines the actual needs of the current driver and passengers, the actual environment of the vehicle, and the real-time scene inside the vehicle, generating corresponding lighting control commands, including the type of light to be controlled and control parameters. Step S6: The mode conflict processor receives the processed data from the preprocessing module and, based on pre-stored conflict handling rules, controls the execution priority of each rule in the scene arbitrator. Step S7: The headlight drive module drives the LED array to output corresponding light effects based on the lighting control commands generated by the scene arbitrator.

[0021] In another aspect, the present invention also provides a computer-readable storage medium storing one or more instructions, the computer instructions being used to cause the computer to execute the above-described multi-source information fusion-based personalized interactive control method for automotive lighting.

[0022] In another aspect, the present invention provides an electronic device, comprising: a memory and a processor; the memory stores at least one program instruction; the processor loads and executes the at least one program instruction to realize the above-mentioned multi-source information fusion personalized interactive control method for automotive lighting.

[0023] The beneficial effects of this invention are as follows: The intelligent automotive lighting control solution provided by this invention, especially for high-pixel intelligent interactive lighting fixtures such as DLP, ADB, and ISD, achieves significant performance improvement and user experience optimization through deep integration of multi-source information and intelligent decision-making. The specific technical effects are reflected in the following core aspects: (1) Significantly improve driving safety: Through high-precision, low-latency voice recognition and control capabilities, drivers can safely and conveniently switch on and off basic lights, adjust brightness, and even switch and trigger complex high-pixel lighting effects without shifting their gaze or manually operating the steering wheel or gear shift in low-light environments (such as at night or in tunnels) or when their hands are occupied (such as operating the steering wheel and gear shift at the same time), effectively avoiding potential accident risks caused by distraction during operation.

[0024] (2) Achieve efficient and complex light effect control: Break through the limitations of traditional voice commands which are limited to simple on / off switching, support natural language command control of rich dynamic light effects (patterns, animations, color sequences), significantly improving the convenience and efficiency of high-pixel light intelligent interaction.

[0025] (3) Optimize road lighting safety and efficiency: The system perceives and understands road conditions in real time (such as highways, urban roads, sharp curves, intersections, and tunnels), makes intelligent decisions, and automatically adjusts lighting parameters (such as brightness, illumination distance / range, and light pattern distribution) and high-pixel projection content (such as curve guide lines and intersection warning signs). For example, highways automatically increase brightness and range; curves achieve dynamic deflection of light angle to reduce blind spots; and intersections intelligently switch between high and low beams / projection interaction signals. This significantly improves the driver's visual clarity and predictive ability, and reduces driving risks under various road conditions.

[0026] (4) Enhanced adaptability and warning effectiveness in adverse weather conditions: In adverse weather conditions such as rain, snow, fog, and sandstorms, the system automatically activates and optimizes lighting modes with stronger penetration (such as fog lights with specific color temperatures), or dynamically adjusts brightness / contrast / projected warning patterns (such as high-visibility light strips and dynamic warning symbols). This significantly improves the vehicle's visibility in low-visibility environments, helping the driver to see the road conditions clearly, while also significantly enhancing the vehicle's warning effect on other road users (vehicles and pedestrians), effectively reducing the risk of collisions.

[0027] (5) Improve driving comfort: The above-mentioned environmental adaptive function operates in full automation. The driver does not need to make frequent manual adjustments. The system continuously provides optimal lighting and warnings, greatly reducing the driving burden and improving overall comfort.

[0028] (6) Enable personalized atmosphere creation: The system can automatically call or generate a matching theme lighting effect library based on specific scenarios (such as festivals, celebrations, user-defined events, etc.). This includes dynamic flashing of reindeer snow scene at Christmas, red auspicious cloud pattern at Spring Festival, and jade rabbit playing with the moon projection at Mid-Autumn Festival. High-pixel lighting vividly renders the atmosphere inside and outside the car, greatly satisfying users' personalized and fun needs and enhancing emotional connection and driving pleasure.

[0029] (7) Achieve immersive audio-visual linkage: The system analyzes the in-vehicle music signal (rhythm, melody, emotional tone) in real time and drives the high-pixel lights to respond synchronously (such as color flowing with the melody, brightness pulsating with the rhythm, and pattern evolving with emotion), creating a highly immersive and coordinated audio-visual entertainment experience, significantly enhancing the entertainment of riding in the car, and to a certain extent helping drivers alleviate the monotony and fatigue of long-distance driving.

[0030] (8) Enhance brand differentiation value: Unique and intelligent scene-based lighting expression becomes an effective carrier for shaping brand personality and technological sense, enhancing product attractiveness and market competitiveness.

[0031] (9) Implement user-centric lighting configuration: The system supports learning and memorizing the personalized preferences of different drivers (such as basic lighting brightness / color temperature settings, lighting effect selection under specific road conditions / weather, favorite music linkage mode, holiday theme preference, etc.).

[0032] (10) Provide seamless adaptation and intelligent recommendation: Based on user profiles, the system can automatically apply personalized lighting settings or intelligently recommend lighting schemes that meet user preferences in specific scenarios, significantly improving driver satisfaction and driving comfort, and enhancing user stickiness and brand loyalty.

[0033] (11) Improve safety and communication efficiency in high-risk scenarios: In special scenarios such as traffic congestion, needing to request lane changing / yielding due to an accident ahead, or vehicle malfunction, the system can automatically trigger or provide preset dedicated high-pixel light interaction signals (such as clearly displaying "THX", "PLS YIELD", or specific warning animations). These standardized light signals become an efficient and safe "communication language between vehicles", clearly conveying the driver's intentions (thank you, request, warning), significantly reducing misunderstandings and conflicts, assisting in maintaining traffic order, and reducing the risk of secondary accidents.

[0034] (12) Enhance vehicle visibility in special road conditions: In complex and chaotic scenarios (such as severe traffic jams or accident scenes), the vehicle can be more easily identified in traffic flow through unique light combinations or high-brightness dynamic markings, thereby further improving safety.

[0035] (13) Automatically execute the optimal safety strategy: The system can intelligently adjust the basic lighting status according to special road conditions (such as automatically adjusting the light pattern to avoid glare when congested), providing additional safety protection. Attached Figure Description

[0036] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0037] Figure 1 This is a schematic diagram of a multi-source information fusion-based personalized interactive control system for automotive lighting provided in Embodiment 1 of the present invention.

[0038] Figure 2 This is a flowchart of a multi-source information fusion method for personalized interactive control of automotive lighting provided in Embodiment 2 of the present invention.

[0039] Figure 3 This is a partial block diagram of the electronic device provided in Embodiment 4 of the present invention. Detailed Implementation

[0040] Before discussing the exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the operations as sequential processes, many of these operations can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but may also have additional steps not included in the figures. The process can correspond to a method, function, procedure, subroutine, subroutine, etc.

[0041] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0042] The present invention will now be described in detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0043] Example 1 To facilitate understanding, the working principle of this system will be generally explained before describing the embodiments of the invention in detail: This embodiment aims to solve the problems of insufficient support for high-pixel lighting fixtures, poor scene adaptability, inefficient interaction, and lack of personalization in existing automotive lighting control systems. This embodiment provides a multi-source information fusion-based personalized interactive control system and method for automotive lighting. This invention constructs a multi-level collaborative control system that achieves scene-based intelligent control of high-pixel automotive lighting fixtures (including DLP digital projection lights, ADB adaptive high beams, ISD intelligent interactive lights, etc.) through a complete link of multi-source data acquisition → intelligent preprocessing → fusion decision → precise execution → closed-loop feedback. The system adopts a cloud-based collaborative architecture, connecting to a cloud-based scene database through V2X / 5G communication, supporting online updates of holiday themes and road condition templates; the underlying layer adopts, but is not limited to, dual-bus control technology, transmitting basic control commands through the CANFD bus and high-pixel light efficiency data streams through Ethernet, ensuring high bandwidth and low latency control. The system deploys a real-time photoelectric feedback mechanism to achieve dynamic calibration and continuous optimization of lighting status. This enables high-pixel automotive lighting to be intelligently and personally adjusted according to complex real-world application scenarios, providing drivers and passengers with a safe, comfortable, and personalized lighting experience.

[0044] The specific implementation method is as follows: like Figure 1 The diagram shown is a structural diagram of a multi-source information fusion-based personalized interactive control system for automotive lighting provided by the present invention.

[0045] As an example, the control system includes: a multi-source sensing module 1, a data preprocessing module 2, a data parsing and processing module 3, and a vehicle headlight driving module 4; the multi-source sensing module 1 is used to collect voice commands, image data, and vehicle application data in real time; the data preprocessing module 2 is used to preprocess the voice commands, converting the voice signal into data information; preprocess the image data to identify user action information and road environment information where the vehicle is located; and preprocess the vehicle application data to obtain one or a combination of dynamic driving data, environmental parameter data, entertainment system data, navigation data, calendar events, and driver behavior data; the data parsing and processing module 3 integrates a rule base 30, a scene arbitrator 31, and a pattern conflict processor 32; the rule base 30 stores... The system stores safety policy rules, environmental adaptation rules, entertainment control rules, and interactive scene rules. The scene arbitrator 31 receives data information processed by the data preprocessing module and, based on the safety policy rules, environmental adaptation rules, entertainment control rules, and interactive scene rules, determines the actual needs of the current driver and passengers, the actual environment of the vehicle, and the real-time scene inside the vehicle, generating corresponding lighting control instructions, including lighting type and control parameters. The mode conflict processor 32 receives data information processed by the data preprocessing module and controls the execution priority of each rule in the scene arbitrator based on pre-stored conflict handling rules. The headlight driving module 4 drives the LED array to output corresponding light effects based on the lighting control instructions generated by the scene arbitrator.

[0046] In some feasible implementations, the multi-source sensing module 1 integrates a sound collector 10, an image collector 11, and an in-vehicle application data collector 12. The sound collector 10 employs a ring-distributed six-microphone array, using beamforming technology to achieve sound source localization, and is used to distinguish voice commands from the driver's seat, the front passenger seat, and other passengers. The image collector 11 includes an in-vehicle camera module 110 and an external camera module 111. The external camera module 111 integrates an embedded target detection algorithm for monitoring the current road conditions of the vehicle. The in-vehicle camera module 110 uses near-infrared technology to monitor the driver's eyelid opening and head posture in real time, detecting fatigue driving conditions. The in-vehicle application data collector 12 is used to acquire heterogeneous data from multiple subsystems of the vehicle's electronic architecture in real time and perform preliminary feature extraction.

[0047] Preferably, the sound acquisition unit 10 employs a six-microphone array arranged in a ring, using beamforming technology to achieve sound source localization and effectively distinguish voice commands from the driver, front passenger, and other passengers. It supports voice capture in complex acoustic environments, including recognizing whispered commands (such as "adjust reading lights to 50% brightness") at speeds up to 120 km / h, and accurately capturing sudden commands (such as "turn on hazard lights") amidst background noise. After capturing these voice commands, the sound acquisition unit transmits them to a subsequent module for processing, namely the voice preprocessing module. The image acquisition unit 11 is equipped with a multispectral camera system (visible light + infrared), covering both in-vehicle driver status monitoring and external environment perception. The external camera has a high dynamic range of 120dB and integrates an embedded target detection algorithm, enabling it to identify pedestrians, vehicles, and road signs within 200 meters in foggy / nighttime conditions, and promptly monitor the vehicle's current road conditions. The in-vehicle camera uses near-infrared technology to monitor the driver's eyelid opening and head posture in real time, detecting driver fatigue. Simultaneously, the image acquisition unit 11 inputs the collected image information to the image preprocessing module. Vehicle Application Data Acquisition Unit 12: This acquisition unit is responsible for acquiring heterogeneous data in real time from multiple subsystems of the vehicle's electronic architecture and performing preliminary feature extraction. The comprehensive data integration covers five major data domains: vehicle dynamic parameters, environmental status, entertainment system, navigation information, and driver behavior. It supports the parsing of multiple vehicle bus protocols such as Ethernet, CAN / CANFD, LIN, and MOST. The vehicle application data acquisition unit acquires real-time dynamic driving data (driving status, control signals, energy management, etc.), environmental parameters (light intensity, meteorological parameters, visual assistance, etc.), entertainment system data (music rhythm value BPM, audio spectrum characteristics, media metadata, etc.), navigation information (route planning, tunnel / bridge location, real-time traffic conditions, etc.), calendar events (automatic recognition of Mid-Autumn Festival, Christmas, and other holiday dates, etc.), and driver behavior data (operation habits, interaction preferences, biometrics), etc.

[0048] Preferably, the multi-source sensing module 1 also integrates a bus interface: used to parse vehicle data such as vehicle speed and steering angle on the CANFD bus, and to obtain the BPM value of the entertainment system via Ethernet.

[0049] Preferably, the multi-source sensing module 1 also integrates an environmental sensor: used to collect environmental parameters such as visibility, light intensity, and precipitation, and supports V2X information access.

[0050] In some feasible implementations, the data preprocessing module 2 integrates a voice preprocessing module 20, an image preprocessing module 21, and an in-vehicle application data preprocessing module 22. The voice preprocessing module 20 is used to perform noise reduction, feature extraction, and voice recognition processing on the collected voice commands, converting the voice signals into data information that a computer can recognize and process. The image preprocessing module 21 is used to perform filtering, enhancement, and target detection processing on the image data, identifying key information in the image, including: driver's action information and / or road environment information where the vehicle is located. The in-vehicle application data preprocessing module 22 is used to preprocess the heterogeneous data collected from various vehicle sensors and vehicle infotainment systems to extract feature data, and to normalize and align the feature data with timestamps.

[0051] Preferably, the speech preprocessing module 20 is used to remove noise contained in the speech command using an adaptive filtering algorithm; to extract phonemes, tones, and / or energy features from the speech command using a speech feature extraction algorithm; and to convert the speech signal into data information using a deep learning speech recognition model. Specifically, the speech information collected by the sound acquisition unit 10 is processed by noise reduction, feature extraction, etc., to convert the speech signal into data information that a computer can recognize and process, thereby improving the accuracy of speech command recognition. The noise reduction process includes: using an adaptive filtering algorithm to dynamically adjust filtering parameters according to the characteristics of background noise, effectively removing background interference such as engine noise and wind noise inside the vehicle. Simultaneously, combined with microphone array technology, the noise reduction effect is further improved by processing signals collected by multiple microphones. The feature extraction process includes: using a speech feature extraction algorithm to extract phonemes, tones, energy, and other features of the speech signal, which will serve as the basis for subsequent speech recognition and semantic understanding. The speech recognition process includes: using a deep learning speech recognition model, such as a hybrid model based on convolutional neural networks (CNN) and recurrent neural networks (RNN), to convert the speech signal into data information. At the same time, the recognition results are optimized by combining language models to improve the recognition accuracy.

[0052] Preferably, the image preprocessing module 21 is used to remove noise from the image using filtering algorithms to improve image clarity; to enhance the contrast and brightness of the image using adaptive enhancement technology to highlight key features in the image; and to detect and identify people, objects, and the environment in the image using a deep learning-based target detection algorithm. Specifically, the image acquired by the image acquisition device 11 is processed by filtering, enhancement, and target detection to identify key information in the image, such as the actions of people and the road environment where the car is located. The filtering process includes: using algorithms such as Gaussian filtering and median filtering to remove noise from the image and improve image clarity; and using techniques such as histogram equalization and adaptive enhancement to enhance the contrast and brightness of the image and highlight key features in the image. The target detection and recognition process includes: using deep learning-based target detection algorithms, such as the YOLO (You Only Look Once) series of algorithms, to detect and identify people, objects, and the environment in the image; and simultaneously, combining image segmentation technology to accurately segment the identified targets to obtain more detailed information.

[0053] Preferably, the vehicle application data preprocessing module 22 is used to detect abrupt changes in data and remove outliers using a sliding window algorithm; to unify data from different buses to the same time base using a clock synchronization protocol; to extract key features from the synchronized data from different buses; and to normalize the key features to generate structured data. Specifically, according to different application scenarios and requirements, the collected data from various vehicle sensors and vehicle systems are preprocessed to extract useful features, such as the relative speed of the vehicle and the rate of change of distance, providing a foundation for subsequent data analysis. Data cleaning: Through statistical analysis methods, outliers and erroneous data are identified and removed. Normalization processing: Different types of data are unified to the same scale range for subsequent data analysis and comparison. For example, data from different sources are unified to the [-1,1] interval and aligned with 10ms timestamps to ensure time sequence consistency.

[0054] In some feasible implementations, the safety policy rules include visibility classification response rules and collision avoidance intervention rules. The visibility classification response rules store the mapping relationship between visibility in environmental parameter data and lighting control commands. The collision avoidance intervention rules store the mapping relationship between the distance to the preceding vehicle and the cut-in angular velocity collected by the image acquisition device and the lighting control commands. The environmental adaptive rules include road condition lighting rules and weather response rules. The road condition lighting rules store the mapping relationship between road condition types collected by the image acquisition device and lighting control commands. The weather response rules store the mapping relationship between weather conditions collected by the in-vehicle application data acquisition device and the lighting control commands. The entertainment control rules include holiday theme linkage rules and audio-visual linkage rules. The holiday theme linkage rules store the mapping relationship between calendar events collected by the in-vehicle application data acquisition device and lighting control commands. The audio-visual linkage rules store the mapping relationship between music features collected by the in-vehicle application data acquisition device and lighting control commands. The interactive scene rules store the mapping relationship between voice interaction commands collected by the sound acquisition device and lighting control commands.

[0055] Preferably, the data parsing and processing module 3 serves as the intelligent decision-making center of the system. Through multimodal fusion and hierarchical decision-making mechanisms, it transforms preprocessed data into high-pixel lighting control commands. This module receives data output from the preprocessing module, including voice and text information, image feature data, and standardized in-vehicle application data. Combining preset rules, machine learning algorithms (such as neural networks, decision trees, support vector machines, etc.), and deep learning models (such as convolutional neural networks, recurrent neural networks, etc.), it performs fusion analysis on multi-source data, extracts potential patterns and rules from the data, and, based on the safety, comfort, and personalized needs of different scenarios, determines the actual needs of the current driver and passengers, the actual environment of the vehicle, and the real-time scene inside the vehicle. It then formulates reasonable and scientific lighting control decisions, specifying the type of light to be controlled (such as DLP, ambient light, ISD, ADB, etc.) and detailed control parameters (such as brightness value, color value, illumination angle, flashing frequency, on / off status, etc.), forming communication signals that the high-pixel vehicle lighting execution module can process, such as CANFD, CAN, LIN, or Ethernet data. While sending lighting control commands, the system also receives feedback signals from the lighting execution module and displays the lighting status information (such as on / off status, brightness value, color, dynamic effects, etc.) on the in-vehicle display screen, allowing users to intuitively understand the working status of the lights and improving the convenience and comfort of use.

[0056] To illustrate this more clearly, consider this example: When a driver is driving a vehicle equipped with this system on Christmas Eve, the system automatically recognizes the calendar information (December 25th, Christmas Day) through the in-vehicle application data collector. Simultaneously, it combines this with the external nighttime environment captured by the image collector (dim lighting, festive street decorations) to trigger the festive scene mode. The data analysis and processing module calls upon a cloud-based Christmas-themed lighting effects library to generate control commands containing dynamic reindeer patterns and flashing snowflake effects, which are transmitted via Ethernet to the high-pixel lighting execution module. At this time, the DLP digital light processing headlights project a slowly moving reindeer silhouette onto the road in front of the vehicle, while the ISD intelligent interactive lights flash red and green alternately to simulate a Christmas wreath effect. The interior ambient lighting simultaneously switches to a warm white gradient mode, creating an overall festive atmosphere. During the journey, if the driver says, "Dim the lights a little," the six-microphone array of the sound collector accurately captures the command. After removing background noise from the in-vehicle music, the audio preprocessing module converts the voice into a three-dimensional control vector: "Reduce brightness by 20%, maintain current color temperature and dynamic mode." The data analysis and processing module, combined with the current vehicle speed (60km / h, not in a high-speed driving scenario requiring strong lighting), quickly adjusts the execution commands. The brightness of the headlights and ambient lights is simultaneously and gently reduced, and the vehicle's screen displays the feedback message "Light brightness adjusted to 80%" in real time. When driving through a section of road with heavy fog, the image acquisition unit detects a decrease in visibility (approximately 30 meters), and the onboard application data acquisition unit simultaneously acquires fog parameters from the weather sensor. The system automatically activates the safety coverage strategy (executing the highest priority safety policy rules), and the high-pixel light execution module adjusts the headlight color temperature to a more penetrating 3000K warm light. At the same time, it projects two high-brightness light bands 10-15 meters in front of the vehicle, clearly marking the lane boundaries; the ISD lights flash slowly in a yellow warning pattern to alert oncoming vehicles. Upon entering the tunnel, the system anticipates the movement based on navigation information and automatically increases headlight brightness 100 meters before the vehicle enters, switching to a tunnel-specific lighting mode. The light is concentrated in the center of the lane, preventing glare from direct sunlight on the tunnel walls. The driver then plays Christmas carols, and the system analyzes the music rhythm (120 BPM) in real time, driving the lights to change synchronously: low-frequency drumbeats trigger red pulses from the ambient lights at the bottom; high-frequency melodies cause the small lights on the rearview mirrors to flash blue, creating an immersive audio-visual experience. After exiting the tunnel and encountering traffic congestion, if a vehicle in an adjacent lane attempts to merge, the system detects the vehicle's angular velocity (exceeding a preset threshold) using a surround-view camera. It immediately projects a red fence pattern onto the road between the two vehicles, conveying the message "merging is temporarily prohibited" without affecting other vehicles, ensuring safe interaction in complex traffic scenarios.

[0057] In some feasible implementations, the data parsing and processing module 3 also integrates a personalized preference engine 33, used to introduce a driver preference profile, store the driver's user ID and preference information, as well as the mapping relationship between the preference information and the lighting control commands, and optimize the lighting control commands based on the stored mapping relationship between the preference information and the lighting control commands during the lighting control process of the scene arbitrator 31. Specifically, the driver preference profile stores data such as music preferences and seat position. The control module optimizes the reminder method accordingly, such as synchronizing the rhythm of the in-vehicle audio system with the LED flashing frequency in music control. This achieves three-dimensional personalized lighting effect decision-making based on "driver biometrics + historical preferences + real-time scene," breaking through the silo of the in-vehicle system and realizing millisecond-level data interaction between the lighting system and entertainment / V2X / ADAS, upgrading lighting to a visual communication language among traffic participants (such as projected yield signs).

[0058] In some feasible implementations, the vehicle headlight drive module 4 includes an LED array, a spectral modulation unit, and a closed-loop feedback component; the closed-loop feedback component collects headlight brightness, temperature, and fault information in real time and sends them to the data parsing and processing module for display.

[0059] Preferably, the vehicle lighting drive module 4 serves as the physical execution terminal of the system. Based on the control bus messages generated by the data analysis and processing module 3, it converts decision commands into precise optical outputs, enabling accurate control of various lights inside and outside the vehicle. The hardware architecture of this module includes an intelligent power supply system (dynamic voltage regulation, overcurrent protection circuit, etc.), a main control MCU unit, a communication interface module (dual-channel design, simultaneously using CANFD for basic commands and vehicle Ethernet for high-pixel lighting effect data streams), a DLP controller, an ADB drive unit (including a high-precision stepper motor and a closed-loop spot positioning system), and an ISD drive unit. Multi-lighting module collaborative control executes commands according to absolute timestamps, ensuring strict synchronization of the dynamic effects of each lighting module. In addition to converting message information into lighting effects, the lighting modules also provide real-time feedback, including real-time monitored actual brightness and temperature, lighting status, and fault status, providing users with a basis for understanding the status of the lighting execution modules.

[0060] The above implementation uses a multi-source perception module to collect heterogeneous data such as voice, images, vehicle status, and environmental parameters. After preprocessing, the data is transmitted to a data parsing and processing module. This module generates control commands based on multi-source data fusion and scene adaptation algorithms. The multi-megapixel driver module then executes the lighting effects, and the system collaborates with other in-vehicle systems through a cross-domain communication interface. This system addresses the shortcomings of existing automotive lighting control systems in terms of intelligence, personalization, and scene adaptability, improving driving safety and interactive experience. It can be widely applied in the field of intelligent connected vehicles.

[0061] It is worth mentioning that all modules involved in this embodiment are logical units. In practical applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. Furthermore, to highlight the innovative aspects of this invention, this embodiment does not introduce units that are not closely related to solving the technical problem proposed by this invention; however, this does not mean that other units are absent from this embodiment.

[0062] Example 2 Please see Figure 2 The above is a flowchart of a method for personalized interactive control of automotive lighting based on multi-source information fusion, provided by an embodiment of the present invention.

[0063] As an example, the control method adopts the multi-source information fusion-based personalized interactive control system for automotive lighting described in Embodiment 1, and the method includes: Step S1: Collect voice commands, image data, and vehicle application data in real time through the multi-source perception module.

[0064] Step S2: The voice command is preprocessed by the data preprocessing module to convert the voice signal into data information.

[0065] Step S3: The image data is preprocessed by the data preprocessing module to identify user action information and road environment information of the vehicle.

[0066] Step S4: The in-vehicle application data is preprocessed by the data preprocessing module to obtain one or a combination of dynamic driving data, environmental parameter data, entertainment system data, navigation data, calendar events and driver behavior data.

[0067] Step S5: Receive the data information processed by the data preprocessing module through the scene arbitrator, and determine the actual needs of the current driver and passengers, the actual environment of the vehicle and the real-time scene inside the vehicle based on the pre-stored security policy rules, environment adaptation rules, entertainment control rules and interactive scene rules, and generate corresponding lighting control instructions, including the type of light to be controlled and control parameters.

[0068] Step S6: Receive the data information processed by the data preprocessing module through the pattern conflict processor, and control the execution priority of each rule in the scene arbitrator based on the pre-stored conflict handling rules.

[0069] Step S7: Drive the LED array to output the corresponding light effect through the light control command generated by the scene arbitrator based on the light driving module.

[0070] It is not difficult to see that this embodiment is a method embodiment corresponding to the first embodiment, and this embodiment can be implemented in conjunction with the first embodiment. The relevant technical details mentioned in the first embodiment are still valid in this embodiment, and will not be repeated here to reduce repetition. Accordingly, the relevant technical details mentioned in this embodiment can also be applied to the first embodiment.

[0071] Example 3 This invention also proposes a storage medium storing a multi-source information fusion-based personalized interactive control method for automotive lighting. When the program for this multi-source information fusion-based personalized interactive control of automotive lighting is executed by a processor, it implements the steps of the multi-source information fusion-based personalized interactive control method for automotive lighting as described above. Since this storage medium adopts all the technical solutions of all the above embodiments, it possesses at least all the beneficial effects brought about by the technical solutions of the above embodiments, which will not be elaborated upon here.

[0072] Example 4 Please see Figure 3 The present invention also provides an electronic device, including: a memory and a processor; the memory stores at least one program instruction; the processor loads and executes the at least one program instruction to implement the multi-source information fusion personalized interactive control method for automotive lighting provided in Embodiment 2.

[0073] The memory 302 and processor 301 are connected via a bus, which may include any number of interconnecting buses and bridges, connecting various circuits of one or more processors 301 and memory 302 together. The bus may also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. A bus interface provides an interface between the bus and the transceiver. The transceiver may be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by processor 301 is transmitted over a wireless medium via an antenna, which further receives data and transmits it to processor 301.

[0074] Processor 301 is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory 302 can be used to store data used by processor 301 during operation.

[0075] The above descriptions are merely embodiments of the present invention. Commonly known structures and characteristics are not described in detail here. Those skilled in the art are aware of all common technical knowledge in the field prior to the application date or priority date, are aware of all existing technologies in that field, and have the ability to apply conventional experimental methods prior to that date. Those skilled in the art can, based on the guidance provided in this application, improve and implement this solution in combination with their own capabilities. Some typical known structures or methods should not be obstacles for those skilled in the art to implement this application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the structure of the present invention. These should also be considered within the scope of protection of the present invention, and will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A multi-source information fusion-based personalized interactive control system for automotive lighting, characterized in that, The control system includes: a multi-source sensing module, a data preprocessing module, a data parsing and processing module, and a vehicle lighting drive module; The multi-source sensing module is used to collect voice commands, image data, and vehicle application data in real time. The data preprocessing module is used to preprocess the voice commands, converting the voice signals into data information; preprocess the image data to identify user action information and road environment information where the vehicle is located; and preprocess the in-vehicle application data to obtain one or a combination of dynamic driving data, environmental parameter data, entertainment system data, navigation data, calendar events, and driver behavior data. The data parsing and processing module integrates a rule base, a scene arbitrator, and a pattern conflict processor. The rule base stores security policy rules, environment adaptation rules, entertainment control rules, and interactive scenario rules. The scene arbitrator is used to receive the data information processed by the data preprocessing module, and based on the safety policy rules, environmental adaptation rules, entertainment control rules and interactive scene rules, it determines the actual needs of the current driver and passengers, the actual environment of the vehicle and the real-time scene inside the vehicle, and generates corresponding lighting control instructions, including lighting type and control parameters. The mode conflict processor is used to receive the data information processed by the data preprocessing module and control the execution priority of each rule in the scene arbitrator based on the pre-stored conflict handling rules. The vehicle headlight driving module is used to drive the LED array to output corresponding light effects based on the light control commands generated by the scene arbitrator.

2. The multi-source information fusion-based personalized interactive control system for automotive lighting according to claim 1, characterized in that, The multi-source sensing module integrates a sound collector, an image collector, and an in-vehicle application data collector. The sound acquisition device uses a six-microphone array distributed in a ring and uses beamforming technology to achieve sound source localization, which is used to distinguish the voice commands of the driver, the front passenger and other passengers. The image acquisition device includes an in-vehicle camera module and an external camera module. The external camera module integrates an embedded target detection algorithm for monitoring the current road conditions of the vehicle. The in-vehicle camera module uses near-infrared technology to monitor the driver's eyelid opening and head posture in real time, and detect fatigue driving status. The in-vehicle application data acquisition device is used to acquire heterogeneous data from multiple subsystems of the vehicle's electronic architecture in real time and perform preliminary feature extraction.

3. The multi-source information fusion-based personalized interactive control system for automotive lighting according to claim 2, characterized in that, The data preprocessing module integrates a voice preprocessing module, an image preprocessing module, and an in-vehicle application data preprocessing module. The speech preprocessing module is used to perform noise reduction, feature extraction and speech recognition processing on the collected speech commands, converting the speech signals into data information that can be recognized and processed by the computer. The image preprocessing module is used to filter, enhance, and detect targets in the image data to identify key information in the image, including: driver's action information and / or road environment information where the vehicle is located; The in-vehicle application data preprocessing module is used to preprocess the heterogeneous data collected from various vehicle sensors and vehicle infotainment systems to extract feature data, and to normalize and align the feature data with timestamps.

4. The multi-source information fusion-based personalized interactive control system for automotive lighting according to claim 3, characterized in that, The speech preprocessing module is used to remove noise contained in the speech command using an adaptive filtering algorithm; to extract phonemes, tones and / or energy features from the speech command using a speech feature extraction algorithm; and to convert the speech signal into data information using a deep learning speech recognition model. The image preprocessing module is used to remove noise from the image using a filtering algorithm to improve image clarity; to enhance the contrast and brightness of the image using adaptive enhancement technology to highlight key features in the image; and to detect and identify people, objects, and the environment in the image using a deep learning-based target detection algorithm. The in-vehicle application data preprocessing module is used to detect abrupt changes and remove outliers using a sliding window algorithm. A clock synchronization protocol is used to unify data from different buses to the same time base; key features are extracted from the synchronized data from different buses. The key features are normalized and structured data is generated.

5. The multi-source information fusion-based personalized interactive control system for automotive lighting according to claim 4, characterized in that, The key features include driving status, control signals and / or energy management data in dynamic driving data; light intensity, meteorological parameters and / or visual aid data in environmental parameters; music tempo value (BPM), audio spectrum characteristics and / or media metadata in entertainment system data; route planning, tunnel / bridge location and / or real-time traffic data in navigation data; holiday data in calendar events; and operating habits, interaction preferences and / or biometric data in driver behavior data.

6. The multi-source information fusion-based personalized interactive control system for automotive lighting according to claim 2, characterized in that, The safety policy rules include visibility classification response rules and collision avoidance intervention rules. The visibility classification response rules store the mapping relationship between visibility in environmental parameter data and light control commands. The collision avoidance intervention rules store the mapping relationship between the distance to the vehicle in front and the cut-in angular velocity collected by the image acquisition device and the light control commands. The environmental adaptive rules include road lighting rules and weather response rules. The road lighting rules store the mapping relationship between the road condition type collected by the image acquisition device and the lighting control command. The weather response rules store the mapping relationship between the weather conditions collected by the vehicle application data acquisition device and the lighting control command. The entertainment control rules include festival theme linkage rules and audio-visual linkage rules. The festival theme linkage rules store the mapping relationship between calendar events collected by the vehicle application data collector and lighting control commands. The audio-visual linkage rules store the mapping relationship between music features collected by the vehicle application data collector and lighting control commands. The interaction scenario rules store the mapping relationship between voice interaction commands collected by the sound collector and light control commands.

7. The multi-source information fusion-based personalized interactive control system for automotive lighting according to claim 6, characterized in that, The conflict handling rules include a higher priority for security policy rules than for environment adaptation rules, which in turn are higher than for interactive scenario rules, which are higher than for entertainment control rules.

8. The multi-source information fusion-based personalized interactive control system for automotive lighting according to claim 7, characterized in that, The data parsing and processing module also integrates a personalized preference engine, which is used to introduce driver preference configuration files, store the driver's user ID and preference information, as well as the mapping relationship between the preference information and the lighting control commands, and optimize the lighting control commands based on the stored mapping relationship between the preference information and the lighting control commands during the scene arbitrator's control of the lights.

9. The multi-source information fusion-based personalized interactive control system for automotive lighting according to claim 1, characterized in that, The vehicle headlight drive module includes an LED array, a spectral modulation unit, and a closed-loop feedback component; the closed-loop feedback component collects headlight brightness, temperature, and fault information in real time and sends them to the data parsing and processing module for display.

10. A method for personalized interactive control of automotive lighting based on multi-source information fusion, wherein the control method employs the personalized interactive control system for automotive lighting based on multi-source information fusion as described in any one of claims 1-9, characterized in that, The method includes: Step S1: Collect voice commands, image data, and vehicle application data in real time through the multi-source perception module; Step S2: The voice command is preprocessed by the data preprocessing module to convert the voice signal into data information; Step S3: The image data is preprocessed by the data preprocessing module to identify user action information and road environment information of the vehicle. Step S4: The in-vehicle application data is preprocessed by the data preprocessing module to obtain one or a combination of dynamic driving data, environmental parameter data, entertainment system data, navigation data, calendar events and driver behavior data. Step S5: Receive the data information processed by the data preprocessing module through the scene arbitrator, and determine the actual needs of the current driver and passengers, the actual environment of the vehicle and the real-time scene inside the vehicle based on the pre-stored security policy rules, environment adaptation rules, entertainment control rules and interactive scene rules, and generate corresponding lighting control instructions, including the type of lighting to be controlled and control parameters. Step S6: Receive the data information processed by the data preprocessing module through the pattern conflict processor, and control the execution priority of each rule in the scene arbitrator based on the pre-stored conflict handling rules. Step S7: Drive the LED array to output the corresponding light effect through the light control command generated by the scene arbitrator based on the light driving module.

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