44809 results about "In vehicle" patented technology

The invention discloses a vehicle-mounted Beidou positioning deviation self-calibration system fused with inertial navigation information, and relates to the technical field of navigation and positioning. The method is used for solving the problems of positioning deviation accumulation and signal failure in a complex environment. The system realizes space-time alignment of inertial navigation and Beidou observation data through timestamp interpolation and coordinate system conversion, and constructs a multi-source synchronous data stream. A difference value between an inertial navigation calculation speed and a Beidou Doppler speed is analyzed based on a vehicle incomplete constraint model, and a weight factor is generated in combination with a confidence threshold to suppress abnormal observation. A geometric matching error is calculated through a high-precision lane map curvature and a vehicle steering angle, and candidate tracks are screened to generate transverse calibration parameters. The noise covariance is dynamically adjusted through a tight coupling filtering algorithm, the mode is switched to a lane constraint calibration mode when a Beidou signal is interrupted, inertial navigation errors are compensated through error boundary constraint, and stable output is achieved. Multi-source data deep fusion and adaptive robust calibration are realized, and system robustness and positioning continuity are improved.

Disclosed are a high-performance loosely-coupled multi-modal data fusion system for a smart driving environmental perception system and a vehicle-mounted device, comprising: a fusion detection model based on a modality-independent feature interaction strategy, which is configured for converting a LiDAR point cloud, a camera image, and a millimeter-wave radar point cloud into a unified bird's-eye view representation, and performing multi-modal fusion; and a fusion tracking model based on a motion-appearance feature cascaded coupling data association strategy, which is configured for performing subsequent trajectory tracking and matching according to multi-modal fusion feature information. A VoD data set and a K-Radar data set are selected for training, verifying, and testing the comprehensive performance of the models, and a TensorRT accelerated inference model is applied, then quantized, and deployed to a vehicle-mounted computational testing platform. The present invention is compatible with mainstream sensor deployment solutions, and achieves the efficient complementary fusion of multi-source heterogeneous sensor information, significantly improving the reliability, accuracy, and adaptability of vehicle-mounted perception systems, thereby effectively responding to extreme operating conditions such as complex traffic scenarios and inclement weather.

The invention provides a risk control big data mining method and system based on the Internet of Vehicles, and the method comprises the steps: collecting a real-time operation data flow of a target vehicle in an Internet of Vehicles environment, including vehicle state time sequence data, a driving behavior event set, external environment perception data and a vehicle-mounted communication interaction record; and then respectively extracting a combined operation feature vector, an abnormal behavior mode identifier and a dynamic environment risk factor from different data, converting to generate a structured communication feature matrix, constructing a space-time association risk analysis map based on the structured communication feature matrix, and carrying out driving risk dynamic assessment on a target vehicle through the space-time association risk analysis map. And finally, generating a risk control decision instruction set according to the result, and feeding back the risk control decision instruction set to the vehicle-mounted control terminal and the Internet of Vehicles cloud platform, thereby realizing comprehensive assessment and effective management and control of vehicle risks.

An improved system and method of defining speeding severity levels, receiving over a wireless network from a vehicle device speed data and metadata, determining associated speeding severity levels, based at least in part on the determined associated speeding severity levels, determining whether an alert communication is to be transmitted the vehicle device, and in response to determining that an alert communication is to be transmitted the vehicle device, transmitting the alert communication via a wireless interface to the vehicle device, the alert communication configured to cause the vehicle device to provide a corresponding in-vehicle alert.

An improved system and method is disclosed of accessing mapping data for a geographical area comprising speed limit data for a first road segment, accessing second mapping data providing speed limit data for a plurality of corresponding road segments from a second source, determining an overlap amount between the candidate road segments and the first road segment, using overlap data to identify a first subset of the candidate road segments, identifying a first speed limit that has a highest likelihood of being accurate from speed limits respectively associated with road segments in the second subset of the candidate road segments, transmitting the first speed limit in association with a map comprising the first geographical area to a vehicle device.

The invention provides a data transmission method and transmission device applied to a vehicle-road cloud collaborative road network system, and the method comprises the steps: collecting vehicle operation state data through a vehicle-mounted terminal, collecting real-time road network topology data through road side equipment, and transmitting the data to a cloud server for data preprocessing; performing feature extraction on the preprocessed data based on a preset time-space correlation model, generating dynamic road network feature data, calling a cooperative transmission model to perform transmission path optimization on the dynamic road network feature data, generating a path optimization parameter set, and performing dynamic adjustment on a target transmission path according to the path optimization parameter set. Generating a self-adaptive transmission path adaptive to the current road network state; and distributing the target vehicle data packet to the roadside node and the cloud node through the self-adaptive transmission path, and carrying out retransmission scheduling on the data packet which is not successfully transmitted based on a feedback confirmation mechanism. According to the invention, the communication efficiency and the system robustness in a vehicle-road cloud collaborative scene can be improved.

The invention provides a vehicle collision early warning method and system based on artificial intelligence, and the method comprises the steps: firstly obtaining a target vehicle multi-source sensor data set which comprises vehicle operation state, environment perception and driving behavior data sequences, each sequence is composed of standardized monitoring data, then carrying out the dynamic spatial-temporal feature extraction of the multi-source sensor data set, and carrying out the early warning of vehicle collision. The method comprises the following steps: generating dynamic space-time coding characteristics containing characteristics such as a vehicle movement trend, calling a pre-trained collision risk prediction model to carry out multi-dimensional risk association analysis on the dynamic space-time coding characteristics to obtain a risk prediction result containing a longitudinal collision risk level and the like, and constructing a real-time early warning strategy set based on the risk prediction result. And finally, deploying the set to a vehicle-mounted decision-making system, establishing a control instruction mapping relation with a vehicle execution mechanism, triggering real-time risk intervention operation, and realizing effective collision early warning and risk intervention.

The invention relates to the technical field of traffic management, in particular to a smart city intelligent traffic management system based on big data, which comprises a multi-source data acquisition module, a data fusion module, a dynamic feature generation module, a situation prediction module, a multi-target optimization module and an instruction execution module, the multi-source data acquisition module acquires multi-source data in real time; the data fusion module generates a standardized traffic data flow and outputs a three-dimensional data cube; the dynamic feature generation module outputs a traffic feature matrix; the situation prediction module generates a dynamic situation map; the multi-objective optimization module outputs an optimization instruction set; and the instruction execution module issues a control instruction to the traffic signal controller, the variable information board and the vehicle-mounted terminal. According to the invention, traffic jam can be relieved, road traffic efficiency can be improved, bus priority scheduling can be optimized, vehicle delay can be reduced, and emergency response speed can be improved.

The invention relates to the technical field of vehicle control, and provides a new energy vehicle operation optimization method and system based on intelligent networking, and the method comprises the steps: collecting a first operation parameter of a vehicle at a current moment and a road parameter at a next moment through a vehicle-mounted sensor network; wherein the first operation parameter comprises a tire movement value corresponding to vehicle suspension deformation variable data based on a current pavement coefficient, and the road parameter is road feature data of a road to be driven in; according to the first operation parameters and the road feature data, a load mean value model and an acceleration safety threshold value of the current vehicle in the road to be driven in are constructed; according to the load mean value model, a damping adjusting instruction of the vehicle suspension is generated; and generating a driving torque vector distribution scheme according to the acceleration safety threshold.

The invention relates to the technical field of power system automation, in particular to a real-time monitoring and fault positioning system for a vehicle-mounted mobile substation. The system comprises a signal acquisition and processing unit which monitors and acquires line parameters and environmental parameters of a transformer substation and traveling wave signals generated when a fault occurs in real time; the fault type identification unit calculates the confidence coefficient of each fault type based on the traveling wave signal so as to judge the fault type generated by the traveling wave signal, and marks the arrival time of the traveling wave head; an algorithm fusion positioning unit preliminarily positions a fault point according to the fault type and the arrival time of a traveling wave head, and then constructs a fitness function through a particle swarm optimization algorithm in combination with line topology and environmental parameters to correct a preliminary positioning error; according to the system, high-precision fault positioning and rapid isolation recovery are realized, the accuracy of fault section division in the complex power distribution network is ensured by modeling switch state and branch change, and misjudgment caused by topological change or equipment overload is effectively avoided.

The invention discloses a road defect detection method based on heavy parameter multi-scale fusion, and belongs to the technical field of target detection in a deep learning neural network, and the method comprises the steps: selecting a data set of road defects from a public data source, and dividing the data set into a training set, a verification set and a test set; a lightweight road defect detection neural network model based on heavy parameter multi-scale fusion is constructed, the model adopts a heavy parameterization technology and a multi-scale feature fusion strategy, and various road defects in the image can be identified and positioned; the constructed neural network model is trained by using the divided training set and verification set, an optimal lightweight road defect detection model is obtained through index evaluation, then reasoning acceleration of the lightweight detection model is carried out, and the lightweight detection model is embedded into an edge computing terminal for deployment; a vehicle-mounted camera is used for collecting image data, collected road images are input into an edge computing terminal to be processed, and types and positions of road defects existing in the vehicle-mounted images are output.

The invention provides a green agricultural product distribution path optimization and inventory control method, and the method comprises the steps: selecting a distribution path with the minimum agricultural product quality influence degree as an optimal path according to a prediction result of the agricultural product quality influence degree, and transmitting the optimized distribution path to a distribution vehicle terminal in real time; after the distribution vehicle terminal receives the optimal distribution route issued by the cloud, the distribution route is navigated in real time through a vehicle-mounted navigation system, a driver is guided to drive along the optimal route, and the distribution route is dynamically adjusted according to the real-time road condition information; meanwhile, according to the real-time position and the driving speed of the distribution vehicle, the arrival time of the agricultural products is dynamically estimated, and the estimated arrival time information is pushed to a destination customer to remind the customer to make preparation for receiving goods in advance.

The invention discloses a multi-screen voice interaction system and method applied in an automobile cabin, and relates to the technical field of vehicle-mounted intelligent interaction, and the system comprises a voice collection unit which is used for carrying out sound source positioning and collection, carrying out the noise reduction processing of a collected voice signal, extracting features from the processed voice signal, and generating a voice feature vector; the multi-modal sensing unit is used for collecting behavior information and physiological state data of a driver and passengers through multi-sensor fusion, so as to extract multi-modal information; the man-machine interaction unit is used for carrying out space-time modeling on the multi-modal information to generate a scene state vector; and the cooperative scheduling unit is used for realizing multi-screen intelligent distribution and cooperative control. According to the invention, voice instructions of a driver and passengers can be accurately identified, the safety and experience of the driver are improved, changes in different driving environments are dynamically adapted, the operation efficiency and user experience of a vehicle-mounted system are improved, and the intelligence and adaptability of the system are improved.

The invention discloses a control strategy model construction method based on big data, and relates to the technical field of intelligent control, a traffic situation deduction and emergency strategy generation module uses a multi-agent traffic simulation technology, simulates a traffic situation in combination with real-time traffic data, evaluates risks and early warns potential crisis by integrating multiple factors, and provides a control strategy model for the traffic situation deduction and emergency strategy generation module. And after an early warning is received, an emergency strategy is generated by using an intelligent algorithm, the strategy is evaluated and optimized by using virtual rehearsal, a result is fed back to a management department, and a synergistic effect with other modules is achieved. Traditional traffic detection equipment data is integrated with multi-source heterogeneous data such as mobile phone signaling, shared bicycle and online hailed bicycle GPS, intelligent vehicle-mounted equipment and public traffic operation, more comprehensive and accurate information such as traffic flow, flow direction, travel behavior track and the like is provided, and rich and accurate basis is provided for traffic management decisions.

The invention relates to the technical field of wireless communication networks, in particular to a vehicle ad hoc network based on intelligent networked vehicle WiFi and a V2V interaction method, a vehicle motion state and network load data are acquired through a vehicle-mounted sensor, AP / STA modes are dynamically switched by combining a deep Q network (DQN) model and an epsilon-greedy strategy, and an adaptive topological structure is constructed. And expanding OLSR protocol message embedding path delay fields, calculating and optimizing multi-hop routing selection in combination with pheromone gradient, and realizing message priority classification and differential privacy protection through a federated learning model. And dynamically adjusting an AP / STA proportion threshold based on a particle swarm algorithm, and triggering node role reconfiguration to balance network load. According to the method, the existing WiFi hardware is reused, the deployment cost is reduced, cross-layer routing optimization and a distributed decision mechanism are combined, and the network real-time performance, the expansibility and the group cooperation capability in a dynamic scene are improved.

The invention provides a vehicle-mounted display terminal man-machine interface interaction experience optimization method and system, and relates to the technical field of vehicle-mounted man-machine interaction, and the method comprises the steps: obtaining eyeball watching, hand operation and head rotation data of a driver, and carrying out the fusion to generate an attention distribution thermodynamic diagram; constructing a dynamic scene recognition model in combination with the vehicle running state and the environment information; and calculating a function module optimization index based on the thermodynamic diagram and scene prediction, and dynamically adjusting the position, the response area and the display level of the function module. According to the invention, the interaction efficiency of drivers can be improved, the operation distraction risk is reduced, and the driving safety is enhanced.

The invention relates to the technical field of data processing, in particular to a data model processing method and system. The method comprises the following steps: acquiring multi-modal vehicle-mounted sensing data, and performing vehicle environment sensing to obtain dynamic vehicle environment sensing data; behavior pattern modeling is carried out through a vehicle-mounted sensing network, and a driver behavior prediction model is obtained; performing driving scene risk assessment based on the driver behavior prediction model and the dynamic vehicle environment perception data to obtain driving scene risk assessment data; performing vehicle multi-level early warning strategy analysis according to the driving scene risk assessment data to obtain a vehicle early warning instruction set; performing driver behavior prediction through a vehicle-mounted sensing network to obtain real-time early-warning driver behavior prediction data; and carrying out vehicle emergency intervention measure automatic decision making on the real-time driving early warning scene simulation data to obtain an emergency intervention decision execution instruction set. According to the invention, the driving safety and the response efficiency can be improved.

The invention relates to the technical field of mobile energy storage vehicle energy management, and discloses a mobile energy storage vehicle energy management method based on an intelligent algorithm, and the method comprises the steps: collecting the charging and discharging rate of a battery pack, the environment temperature, the power grid load fluctuation and other multi-dimensional energy state data; a heterogeneous layered architecture of edge computing nodes, a cloud collaboration platform and a vehicle-mounted control terminal is constructed, energy partitions are divided according to peak valleys of a power grid, and low-delay response, global optimization and local closed-loop control modes are configured; fusing the multi-source heterogeneous energy data streams and removing abnormal values; a dynamic optimization decision model is constructed through a battery degradation model and a power grid supply and demand balance equation, and a multi-parameter collaborative constraint relation is solved through simultaneous solving of a multi-target iteration solver and a parallel gradient descent algorithm; and generating a three-level adaptive response instruction sequence of local battery overload alarm, regional power grid frequency modulation early warning and global energy scheduling imbalance pre-judgment based on constraint boundary triggering conditions. According to the method, the accuracy, the collaboration and the robustness of energy management are improved.

The invention provides a modeling method and system for a user charging behavior portrait. Wherein the charging time point, the driving mileage and the battery attenuation data of a user are collected, a regional charging demand thermodynamic diagram is generated in combination with a driving track, and the electric energy consumption and the battery attenuation data are fused to form energy consumption characteristics. In an underground parking lot and other satellite signal missing areas, a differential positioning base station and vehicle-mounted inertial navigation are used for compensating positioning, and continuous space-time trajectory coordinates are generated. Energy consumption characteristics and track coordinates are analyzed through deep reinforcement learning, the correlation among the charging time period, the pile distribution density and the power grid load is identified, and a charging demand space-time weight coefficient is generated; and constructing a user charging behavior feature vector based on the coefficient and power grid data, performing similarity matching classification, and finally establishing a user charging behavior portrait model. According to the technical scheme provided by the invention, accurate prediction of the charging demand and space-time collaborative optimization of the power grid load are realized.

The invention discloses a vehicle positioning method and system based on UWB and vehicle constraint, and relates to the field of vehicle positioning, and the method comprises the steps: deploying a UWB reference station in a well mining environment, and enabling a vehicle to be provided with a UWB tag; obtaining distance measurement data of at least two UWB reference stations, and detecting and compensating the distance measurement data to obtain a vehicle UWB position; collecting vehicle-mounted IMU (Inertial Measurement Unit) data, and obtaining a vehicle attitude quaternion through attitude calculation; collecting vehicle wheel speed meter data, and correcting the vehicle wheel speed meter data according to vehicle motion constraint conditions; according to the vehicle UWB position, the vehicle attitude quaternion and the corrected vehicle wheel speed meter data, the vehicle position is obtained through an adaptive Kalman filtering fusion algorithm. Aiming at low positioning precision caused by easy accumulation of errors and drifting of a traditional inertial navigation system in a well mine closed space in the prior art, the invention reduces the influence of a multipath effect in the traditional inertial navigation system on the positioning precision.

The invention belongs to the technical field of digital information transmission, and relates to a vehicle-mounted ad hoc network security communication system and method based on an NTRU lattice cryptosystem, and the system comprises a message signature module, a message encryption module, a message decryption module, an identity authentication module, and a key exchange module. The message signature module carries out signature by utilizing a private key and a random polynomial dynamically generated by a pseudo-random number generator based on an NTRU lattice cryptosystem; the message encryption module encrypts a to-be-sent message by randomly selecting a temporary polynomial and combining a public key of a receiver; the message decryption module is used for decrypting the received ciphertext # imgabs1 # by using a private key and an inverse element # imgabs0 # of a module p of the private key; the identity authentication module is used for performing identity authentication; the key exchange module realizes secure key exchange between communication nodes in the vehicle-mounted ad hoc network through an improved Blom key distribution protocol. The system improves the security and communication efficiency of the vehicle-mounted network.

The invention discloses a scene and preference-based vehicle-mounted multimedia intelligent recommendation method and device and a vehicle, and the method comprises the steps: detecting the starting of the vehicle, detecting the current operation state data and surrounding environment data of the vehicle in real time through a vehicle-mounted sensor, and collecting the driving state information of a driver; determining a current driving scene of the vehicle based on the detected current running state data and surrounding environment data of the vehicle; determining the current driving emotion of the driver based on the collected driving state information of the driver; and based on the current driving scene of the vehicle and the current driving emotion of the driver, controlling a vehicle-mounted multimedia to automatically recommend media information matched with the current driving scene and the current driving emotion of the driver, and playing the media information. The invention provides a vehicle-mounted multimedia intelligent recommendation and seamless switching method based on scene perception and user preference, and convenience is provided for use of a user.

The invention provides a dynamic calibration method and system for a vehicle-mounted emotion recognition system, and the method comprises the steps: S1, obtaining multi-source data which comprises a facial image, a voice signal and a physiological signal; the obtained multi-source data are preprocessed, and preprocessed multi-source data are obtained; s2, performing feature extraction based on the preprocessed facial image, the voice signal and the physiological signal to obtain a facial expression feature vector, an audio feature vector and a physiological state feature vector; s3, evaluating the current environment credibility based on an environment credibility evaluation function; s4, dynamically distributing the weight of the multi-source data according to the credibility of the current environment and the real-time scene; and S5, constructing a multi-modal fusion vector based on the dynamically distributed weight of the multi-source data, the facial expression feature vector, the audio feature vector and the physiological state feature vector, and performing emotion recognition by using the constructed emotion recognition model based on the multi-modal fusion vector.

The invention relates to the technical field of vehicle-mounted temperature control, and discloses a method and system for intelligently controlling the temperature and humidity of a vehicle-mounted cold chain and a storage medium. The method comprises the following steps: acquiring and preprocessing temperature and humidity data through a sensor; performing state evaluation based on a three-way decision model; constructing a temperature and humidity trend analysis model to predict a change trend; constructing a control decision system by adopting multi-agent reinforcement learning; executing a multi-mode control strategy; and adaptively updating the control system according to the execution effect. According to the application, the future temperature and humidity change trend is predicted according to the historical data and the real-time state, collaborative optimization control of temperature and humidity is realized, self-learning and continuous optimization capabilities are provided, and the accuracy, stability and energy utilization efficiency of cold chain temperature and humidity control are effectively improved.

The invention belongs to the technical field of intelligent driving, and particularly relates to an intelligent driving method and system with a body, and the method comprises the steps: fusing the V2X data of a vehicle-mounted laser radar, a millimeter wave radar and a road side unit, and obtaining the vehicle driving information; constructing a centimeter-level dynamic environment model by adopting a space-time alignment algorithm, and realizing multi-source data space-time synchronization and obstacle real-time prediction; designing a hierarchical reward function including security, efficiency and comfort rewards based on a user risk cognition mechanism, and generating an end-to-end reinforcement learning decision in combination with a Transform architecture; optimizing the dynamic environment model through reinforcement learning decision; inputting vehicle driving information into the optimized centimeter-level dynamic environment model, and predicting obstacles and vehicle tracks in a vehicle driving path; an MPC-Hybrid feedforward-feedback control system is constructed, a feedforward module is adopted to calculate a front wheel turning angle and acceleration parameters according to obstacles in a vehicle driving path and a vehicle track, and a feedback module is adopted to optimize transverse and longitudinal errors; controlling the driving direction of the vehicle according to the front wheel turning angle and the acceleration parameter transverse and longitudinal errors; according to the invention, through deep fusion of vehicle-road collaborative perception and reinforcement learning decision, the reliability and adaptability of the automatic driving system are significantly improved.

The invention relates to the technical field of intelligent driving, and discloses an intelligent networked automobile multi-modal sensing and decision-making system based on deep learning, and the system comprises a multi-modal data collection module which is used for collecting image data and radar time sequence data around a vehicle through a vehicle-mounted camera and a vehicle-mounted radar sensor, the surrounding environment of the vehicle can be sensed in all directions; the image data processing module is used for processing the image data by using an improved deep learning model and extracting target object feature information in the image; the radar data processing module is used for analyzing the change trend of the radar time sequence data and making an optimal driving decision according to different traffic scenes in combination with the feature information of the target object; and the optimal decision execution module is used for executing an optimal decision action according to the optimal driving decision. According to the invention, high-precision perception and quick response to environment information in a complex traffic scene can be realized, the driving safety is improved, and the traffic efficiency is optimized.

The invention discloses a cargo transportation real-time path planning method based on artificial intelligence, and relates to the technical field of artificial intelligence and intelligent traffic, and the method comprises the following steps: collecting real-time data from a traffic monitoring system, a vehicle-mounted sensor, a road event platform and a weather information system in a transportation process, forming an original data set containing multi-source information; time synchronization and space alignment are carried out on source data in an original data set, and a standardized data frame is constructed to ensure the consistency of different source information in a space-time dimension. According to the method, the accuracy, the real-time performance and the intelligence of path planning are effectively improved through multi-source data consistency identification, weighted fusion and a dynamic re-planning mechanism. The system can timely identify data conflicts, dynamically adjust paths and enhance transportation safety and efficiency, has a self-learning capability, continuously optimizes path strategies and adapts to complex traffic environments.

The invention relates to the technical field of vehicle monitoring, in particular to a multi-dimensional information fusion vehicle monitoring and abnormal behavior early warning method, which comprises the following steps: acquiring real-time vehicle operation data, environment perception data and driver behavior data; performing time-space synchronization and feature fusion on the vehicle operation data, the environment perception data and the driver behavior data to generate a multi-dimensional fusion feature vector; inputting the multi-dimensional fusion feature vector into a preset abnormal behavior detection model, and outputting an abnormal probability value of the current driving scene; and if the abnormal probability value exceeds a preset threshold value, generating a graded early warning signal and carrying out early warning prompt through a vehicle-mounted interaction interface and a cloud platform. The problems that an existing vehicle monitoring and abnormal behavior early warning method is single in data source, low in monitoring accuracy and incapable of achieving real-time monitoring are solved.

A system and method for ΔI-powered engine diagnostics combine OBD-II data, high-frequency sound, and vibration analysis to detect engine wear and mechanical faults. A detachable puck sensor in the engine bay captures sound and vibration signals, while an OBD-II module collects engine performance metrics. A smartphone app collects and uploads data to a backend AI engine, where Dynamic Time Warping (DTW) aligns event-driven or asynchronous data and time series data from multiple sources, ensuring accurate feature fusion. Machine learning models then detect engine wear, belt degradation, knocking, and bearing faults, generating a diagnostic report with severity assessments and predictive maintenance recommendations. By integrating multi-modal sensor data, this system enhances early-stage fault detection beyond traditional OBD-II diagnostics, offering greater accuracy in assessing physical wear conditions and optimizing vehicle maintenance.

The invention relates to the technical field of intelligent control, and discloses a vehicle-mounted atmosphere lamp control method and system based on environmental perception. The method comprises the following steps: acquiring in-vehicle image data and vehicle-mounted sensor data, and inputting the data into a double-branch Mama information fusion module for feature extraction and fusion processing to obtain a fusion feature map; performing situation recognition and analysis on the fused feature map to obtain a driving situation feature vector and a situation incidence matrix; according to the driving situation feature vector and the situation incidence matrix, vehicle-mounted atmosphere lamp parameters are decoupled, and decoupled illumination parameters are obtained; and performing situation self-adaptive lighting regulation and control and dynamic situation weighted loss calculation on the decoupled lighting parameters and the driving situation feature vectors to obtain target lighting control parameters. According to the invention, the problem of unbalanced illumination requirements in different driving scenes is solved, and the adjustment of the vehicle-mounted atmosphere lamp is more accurate.