261 results about "Dynamic vehicle" patented technology

The invention relates to the field of battery state evaluation, in particular to a new energy automobile battery health state evaluation method and system. The method comprises the following steps: acquiring a vehicle battery multi-mode state monitoring parameter and a historical vehicle endurance mileage log; performing battery state dynamic evolution on the vehicle battery multi-mode state monitoring parameters to obtain battery multi-mode state evolution characteristics; carrying out multi-time-point maximum endurance calculation on the historical vehicle endurance mileage log, carrying out time sequence trend evolution, and constructing a vehicle endurance evolution trend chart; performing nonlinear attenuation trend analysis on the vehicle endurance evolution trend graph, and performing battery aging mechanism evolution analysis so as to construct a battery state aging evolution knowledge graph; according to the battery state aging evolution knowledge graph, environment change influence analysis is carried out on the vehicle endurance evolution trend graph, dynamic vehicle state modeling is carried out, and a dynamic vehicle twinborn model is constructed. According to the invention, efficient and accurate battery health state evaluation is realized.

Systems and methods are provided herein for implementing dynamic, scalable platoon formations for traveling vehicles. Such platoons may be formed and modified on the fly by vehicles that have varying sensing or autonomy capabilities. Platoons may be negotiated among available vehicles, to designate roles including global platoon leader, local leader(s), and platoon member(s). While the platoons are in place and the vehicles traveling in coordination, platoon members may depart the platoon, and the remaining members may re-allocate roles among the remaining members.

The invention discloses a dynamic vehicle scheduling intelligent decision-making system based on big data, and relates to the technical field of intelligent traffic and logistics scheduling, and the system comprises a data collection module, an event analysis module, a knowledge graph construction module, a hypergraph modeling module, a constraint processing engine, an optimization decision-making module, a strategy verification module and an output interaction module. The system has the advantages that multi-source data such as government announcement texts and social media information are acquired in real time through the data acquisition module, event key information is extracted through the event analysis module by utilizing a natural language processing technology, and an event knowledge graph of an incidence relation is constructed through the knowledge graph construction module; the strategy verification module simulates and verifies the strategy effect through the digital twinning technology and iteratively optimizes the strategy effect, the whole process does not need manual intervention to adjust rules, the limitation that a traditional system depends on manual processing is broken through, and the problems that dynamic strategy adjustment is time-consuming, labor-consuming and error-prone in an extreme scene are solved.

The invention relates to the technical field of intelligent traffic supervision, and discloses a vehicle tarpaulin coverage real-time detection method based on multi-modal feature fusion, and the method comprises the following steps: S1, constructing a bimodal input data stream; s2, the bimodal images are preprocessed respectively; s3, extracting features; s4, dynamically fusing the bimodal features through an adaptive feature fusion module; s5, inputting the mixed feature map into a lightweight convolution detection network, and outputting a segmentation mask; s6, motion trail compensation is carried out on the dynamic vehicle; by designing a multi-modal adaptive feature fusion mechanism, the robust detection performance in a complex environment is improved: the fusion weight is dynamically adjusted based on the environment illumination and the temperature gradient, so that the system automatically strengthens effective modal features under extreme conditions such as strong light, night, rain and fog and the like; by introducing a dynamic motion compensation framework, the problem of motion fuzzy interference of a running vehicle is effectively solved, and the boundary precision of dynamic vehicle tarpaulin coverage detection is improved in a breakthrough manner.

The invention provides a road vehicle real-time twinning method and system based on a monocular camera, which can sense the change of a multi-target vehicle in a dynamic scene in real time by combining target detection, target tracking and depth estimation technologies, and accurately map the position and the feature of the multi-target vehicle to a virtual twinning scene, so that the real-time twinning of the multi-target vehicle is realized. And the defect of dynamic vehicle twinning support in the existing road twinning scene is overcome. The vehicle features are extracted by adopting a visual question and answer large model, dependence on specific task data and scenes is reduced, generative reasoning and multi-round interaction are supported, problems can be dynamically adjusted, and a new scene can be quickly adapted.

The invention belongs to the technical field of communication, and particularly relates to a heterogeneous vehicle edge computing resource allocation method based on deep reinforcement learning, which comprises the following steps: constructing a heterogeneous VEC system model; establishing an optimization problem model by taking minimization of system utility under resource constraint and URLLC constraint as a target; modeling an unloading and resource allocation process as a DRL process, and constructing a deep reinforcement learning framework to obtain a state space, an action space and a return function; based on a deep reinforcement learning framework, optimizing a resource allocation strategy through an improved SAC algorithm to obtain an optimal communication resource and calculation resource allocation proportion; according to the method, the URLLC requirement is met, meanwhile, the system utility is remarkably reduced, the method is suitable for a dynamic vehicle environment, and an effective solution is provided for heterogeneous vehicle edge computing resource allocation.

The invention discloses an Internet of Vehicles low-delay federated learning method based on semi-asynchronous communication, and the method comprises the steps: constructing a vehicle-road cloud cooperative federated learning framework and a system performance model, building a federated learning training time delay optimization model, minimizing the overall training time delay, and meeting the vehicle energy consumption upper limit and global model precision constraint conditions; a multi-dimensional priority dynamic vehicle selection mechanism is realized, and probability sampling is carried out by using a roulette selection algorithm; a vehicle-RSU federated learning strategy based on semi-asynchronous communication and knowledge distillation optimizes the problem of lagging behind and the problem of model obsolessness; a cloud model aggregation opportunity is optimized based on a model difference metric and an adaptive cloud aggregation strategy of a dynamic threshold trigger mechanism. According to the method, collaborative optimization is carried out on multiple levels of equipment selection, intra-layer communication aggregation, old model processing, inter-layer aggregation triggering and the like, so that the overall training time delay of the Internet of Vehicles federated learning system can be effectively reduced on the premise of satisfying model precision and equipment energy consumption constraints.

The invention discloses a method for automatically measuring the driving sight distance of a highway curve. The method comprises the following steps: S1, acquiring three-dimensional space data and vehicle motion parameters of a curve area through a multi-modal sensor; the method comprises the steps of S1, obtaining three-dimensional space data and vehicle motion parameters, S2, carrying out multi-source data fusion on the three-dimensional space data and the vehicle motion parameters, S3, constructing a driving safety sight distance calculation model based on a dynamic vehicle kinematics model, and S4, dynamically correcting a safety sight distance calculation result according to real-time environment parameters. In the frequency domain, double-domain filtering is guided through attention to effectively suppress interference, the purity and accuracy of signals are improved, compared with a traditional single-domain processing method, the double-domain filtering mode can more accurately remove signal interference generated by dynamic interference sources such as construction marks, the reliability of subsequent data processing and decision making is ensured, and the method is suitable for popularization and application. More accurate data support is provided for curve identification and sight distance measurement, and the robustness of the system in a complex environment is improved.

The present invention discloses an active collision avoidance method and vehicle-mounted electronic equipment based on a multi-level risk fusion potential field, comprising the following steps: 1: obtaining information about the vehicle and the surrounding environment; 2: predicting the driving trajectory of the vehicle and other vehicles; 3: judging the positional relationship between the trajectories of the vehicle and other vehicles; 4: if the trajectories intersect, calculating the intersection point in a fixed coordinate system; if the trajectories are parallel and there is a possibility of collision, calculating the rear-end collision point; 5: designing and constructing a risk domain of lane boundaries, static obstacles, dynamic vehicles, estimated trajectory intersection points and collision points in the surrounding environment; 6: performing real-time curvature constraints on the collision avoidance path, and planning a virtual force range within a small time period; 7: determining the optimal virtual force within the current time period based on the principle of low driving risk, and planning a real-time optimal local path; 8: obtaining continuous collision avoidance trajectories based on the optimal virtual forces planned in real time in different time periods; 9: repeating the above steps until the vehicle reaches a safe position.

The invention discloses a logistics vehicle scheduling method and system, electronic equipment and a storage medium, and relates to the technical field of intelligent logistics, and the method comprises the steps: in a cloud center layer, an initial vehicle scheduling scheme is generated based on a dynamic vehicle scheduling model, and the dynamic vehicle scheduling model aims at minimizing the transportation cost; and the edge node layer receives the real-time traffic data and the order information, and dynamically adjusts the initial vehicle scheduling scheme to generate a dynamic scheduling scheme. And the vehicle-mounted terminal layer receives the dynamic scheduling scheme and performs path optimization based on the sensor data to obtain the execution state of the vehicle-mounted terminal. The execution state of the vehicle-mounted terminal is fed back to the edge node layer and the cloud center layer to update the dynamic vehicle scheduling model, a target vehicle scheduling scheme is determined based on the updated dynamic vehicle scheduling model, and the target vehicle scheduling scheme is used for scheduling multiple logistics vehicles. Through cloud side-end cooperative processing, the decision-making quality of logistics vehicle scheduling and the system response speed are improved.

The invention discloses an expressway dynamic vehicle behavior trajectory tracking induction monitoring system, which relates to the technical field of intelligent traffic monitoring, and comprises a multi-source data acquisition module used for responding to a trigger signal of a vehicle entering a monitoring area, acquiring multi-source data, and sending the multi-source data to the monitoring area; the multi-source data comprises three-dimensional point cloud data, a thermal infrared image, a multi-view visible light image, vehicle speed and lane data and a license plate image; and the preprocessing and calibration module is used for registering and fusing the thermal infrared image and the visible light image. According to the expressway dynamic vehicle behavior trajectory tracking induction monitoring system provided by the invention, through multi-source sensor fusion and intelligent algorithm innovation, high-precision tracking and real-time monitoring of vehicle behavior trajectories are realized, road safety and traffic efficiency are effectively improved, and the expressway dynamic vehicle behavior trajectory tracking induction monitoring system is suitable for popularization and application. Multi-dimensional sensors such as a laser radar, an infrared thermal inductance instrument and a visible light camera are adopted for cooperative work, and the capability of capturing vehicle information in a weak light environment is enhanced.

A vehicle includes a chassis, a vehicle body mounted to the chassis, a display device within the vehicle body, and a control system coupled to the chassis and the display device. The control system is configured to detect a change to at least one of the chassis or the vehicle body. The control system is further configured to generate a three-dimensional scene including a representation of a three-dimensional model the vehicle, the three-dimensional scene corresponding to the change. The control system renders the three-dimensional scene to obtain an image and outputs the image on the display device. The image may be rendered using cel shading. The three-dimensional scene may further correspond to an environment of the vehicle such as weather, landscape, and time of day.

The invention provides an intelligent optimization method and system for a parking lot system. Wherein parking space pressure change data is collected through a parking space pressure sensor, vehicle size data of a vehicle identification system is synchronized, and a three-dimensional thermodynamic diagram containing parking demands is constructed based on spatial distribution; the stand column displacement sensing array monitors the height of a vehicle chassis, a dynamic vehicle size database is constructed by combining size data, and parking space physical constraints are matched; in cooperation with the parking demand and the constraint condition, screening and matching an idle parking space set according to the size of the entering vehicle and the thermodynamic diagram prediction trend; and a vehicle guiding path is generated by combining the incidence relation between the historical parking path and the thermodynamic diagram and integrating the parking space position and the path occupation state, so that intelligent optimization of the parking lot is realized. According to the technical scheme provided by the invention, precise parking space matching and dynamic path guiding of vehicle size self-adaption are realized, and the turnover efficiency and the space utilization rate of the parking lot are improved.

The invention discloses an Internet of Vehicles multi-hop unloading optimization method and system based on a graph neural network, and the method comprises the steps: system initialization and environment modeling, task unloading strategy decision making, multi-hop path construction, link state detection and RIS configuration, and task transmission and execution, the system comprises a path construction module, a link enhancement module and a task unloading scheduling module, multi-target and multi-granularity unloading cooperative scheduling of tasks among local, adjacent vehicles, roadside units and a cloud is supported by sensing node positions and link state changes in the dynamic Internet of Vehicles, and compared with a traditional fixed strategy or single agent method, the task unloading cooperative scheduling efficiency is greatly improved. The method provided by the invention has higher path robustness, stronger link adaptability and better scheduling performance, and is suitable for scenes with high requirements on calculation timeliness and communication stability, such as automatic driving and smart traffic.

The invention relates to the technical field of carbon emission evaluation, and relates to a carbon emission evaluation method and system based on multi-source heterogeneous data, and the method comprises the steps: obtaining the space matching data of a target region boundary and parking spaces, and generating the number and distribution information of effective parking spaces; the time sequence dynamic remote sensing data, the traffic density fluctuation data and the demographic data are fused, a parking space activeness factor model is constructed, and the parking space use intensity is output; predicting the vehicle type distribution proportion of the target area by using a neural network, and calculating the annual average driving distance of the vehicle; calculating a dynamic vehicle carbon emission factor and a static vehicle carbon emission factor according to the time dynamic nature and the regional difference of the power grid energy; and calculating the total carbon emission of the target area by integrating the vehicle type distribution proportion, the annual average driving distance and the carbon emission factor based on a collaborative correction mechanism of the parking space resource tensity and the parking space use intensity. The objective of the invention is to realize community-level high-precision carbon emission accounting and improve the spatial resolution and adaptability of carbon emission accounting.

The invention relates to the technical field of ecological driving, in particular to a self-adaptive ecological cruise method and device based on dynamic vehicle behavior prediction.The method comprises the steps that on the basis of at least one driving parameter of a surrounding vehicle within a vehicle target range, the transverse motion state of the surrounding vehicle is recognized, predicting a dynamic driving track of the surrounding vehicle in the future target time according to the transverse motion state; in combination with the at least one driving parameter and the dynamic driving track, generating a vehicle distance variation between the surrounding vehicles in the future target time; and generating a self-adaptive ecological cruise strategy of the vehicle at the current moment based on the dynamic driving track, the vehicle distance variation and a preset driving constraint condition. Therefore, the problems that the prediction dimension of the ecological cruise control strategy of the new energy automobile is still limited to the longitudinal motion state change, the decision complexity of the new energy automobile as an intelligent driving main body is ignored, and obvious insufficient environmental adaptation is achieved in a dynamic traffic scene in the related technology are solved.

The invention discloses a driving sunscreen cooling control method based on sun illumination tracking, and the main design concept of the method is that the recognition precision of the relation between dynamic light and a dynamic vehicle is improved through sun vector space modeling. Specifically, accurate measurement and calculation of the sun angle is matched with dynamic response of a vehicle system to realize a sunlight tracking type sunscreen mode, and the method mainly comprises the following steps: acquiring data such as longitude and latitude, date time, advancing direction and the like of a current vehicle, calculating an irradiation angle of sunlight irradiated to the vehicle, and combining a real-time measurement value of a light sensor to obtain a sunlight tracking type sunscreen mode. And deciding whether the vehicle enters the sunscreen mode at the current moment. On the basis of real-time tracking of the sun position and by means of intelligent cooperative control of devices such as an electric sun shield curtain, electrochromic glass and air conditioner directional blowing, dynamic and accurate automatic non-inductive sun protection and cooling are achieved, and comfortable driving and riding experience is brought to drivers and passengers.

The invention discloses a configurable dynamic vehicle-mounted dimming method and system for tunnel lamp illumination, and the method comprises the following steps: dividing a target tunnel into a plurality of dimming sections, further dividing a dimming loop in each dimming section, and carrying out the deployment of an illumination unit and an incoming vehicle detector based on a division result; a dimming counting interval of a logic level is constructed, a dynamic mapping relation is established between the dimming counting interval and a dimming loop of a physical level, and the dimming counting interval is a minimum logic unit used for vehicle tracking and counting; an independent vehicle counter is configured for each dimming counting interval, and a counting updating rule is set; configuring a security policy for counting function activation and dimming execution; and responding to the triggering of the coming vehicle detector, updating the vehicle count in the dimming counting interval in real time, and adjusting the brightness of the dimming loop based on the security policy. Compared with the prior art, the configurable dynamic vehicle-mounted dimming method and the configurable dynamic vehicle-mounted dimming system have remarkable progress in the aspects of energy conservation and safety.

The invention relates to the technical field of intelligent mine and vehicle safety, in particular to a vehicle collision prediction method and prediction system for a mine laneway environment. The vehicle collision prediction method for the mine laneway environment comprises the following steps: constructing a static laneway environment map based on a mine digital map; at each time step t, constructing a dynamic vehicle interaction diagram according to the vehicle positioning information; extracting the spatial-temporal characteristics of the dynamic vehicle interaction diagram and the state characteristics of the driver, fusing the spatial-temporal characteristics and the state characteristics to form a comprehensive characteristic vector, and outputting a predicted value of the running speed of the current target vehicle at the next moment through a regression layer; and judging whether a collision risk exists or not according to the deviation between the predicted value of the vehicle speed and the real speed. According to the method, the space-time diagram neural network is constructed, vehicle dynamic interaction, static roadway environment topology and driver state multi-source information are fused, and high-precision and advanced prediction of the collision risk is achieved.

Provided are system, method, and device for automatically manage communications between vehicles. According to embodiments, the system may include: a memory storage storing computer-executable instructions; and at least one processor communicatively coupled to the memory storage, wherein the at least one processor may be configured to execute the instructions to: receive an identification of a target vehicle from a server; obtain an identification of a nearby vehicle from the nearby vehicle; determine whether the identification of the target vehicle matches the identification of the nearby vehicle; and in response to determining that the identification of the target vehicle matches the identification of the nearby vehicle, transmit a current time and location to the server.

The embodiment of the invention provides a vehicle light control method and device, equipment and a storage medium. Behavior information of a user in a vehicle and vehicle environment information are obtained; light exists in the vehicle; according to the behavior information and the vehicle environment information, determining a target image used for describing a scene where the user is located currently; determining brightness information, color information and image information corresponding to the target image; the image information is used for representing an image effect corresponding to a scene in the target image; determining target lamplight in the lamplight of the vehicle according to the brightness information and the image information corresponding to the target image; determining the target brightness and the target color corresponding to the target light according to the brightness information and the color information corresponding to the target image; and controlling the target light according to the target brightness and the target color corresponding to the target light. According to the embodiment of the invention, user behaviors and environment changes can be responded in real time, the in-vehicle light effect is enriched, and intelligent and dynamic vehicle light control is realized.

A fiber optic sensing cable located along a side of a paved road and runs parallel to a driving direction is monitored by distributed fiber optic sensing (DFOS) using Rayleigh backscattering generated along the length of the optical sensor fiber cable under dynamic vehicle loads. The interaction of vehicles with roadway locations exhibiting distressed pavement generates unique patterns of localized signals that are identified / distinguished from signals resulting from vehicles operating on roadway exhibiting a smooth pavement surface. Machine learning methods are employed to estimate an overall road surface quality as well as localizing pavement damage. Power spectral density estimation, principal component analysis, support vector machine (SVM) combined with principal component analysis (PCA), local binary pattern (LBP), and convolutional neural network (CNN) are applied to develop the machine learning models.

The invention relates to the technical field of state monitoring of rail transit equipment, and discloses a rack rail train gear online detection system and method, and the system comprises a laser sensor array module; a visual perception assembly module; a trigger module; and a data processing module. The method comprises the following steps: synchronously acquiring vertical distance data and an image sequence; applying an optical flow algorithm to track an image sequence to calculate a gear rotation angle; installing errors and vehicle vibration are separated through cross-correlation and low-pass filtering, and distance data are corrected; reconstructing the full-circle geometric contour of the gear according to the rotation angle; and finally, geometric deviation and surface damage are judged based on contour and image analysis. According to the invention, through multi-modal data fusion and error decoupling, high-precision automatic on-line detection of the gear of the rack rail railway vehicle under the dynamic passing working condition of the vehicle is realized, and the problems that the traditional off-line dismounting detection efficiency is low and early damage cannot be found in time are solved.

The invention belongs to the technical field of emergency management, and particularly discloses a medical waste classification recovery vehicle path optimization method and system. Comprising the steps that according to infection risk characteristics of different types of medical wastes, an infection risk calculation model for implementing differentiation in the temporary storage, collection and transportation processes of the medical wastes in a medical institution is constructed; constructing a time dependence function under the time-varying road network, and depicting the dynamic change of the driving speed of the recovery vehicle in different time periods; considering exposed groups in road dynamic vehicles, and establishing an infection risk dynamic calculation model in and around the road; constructing a multi-objective optimization model with the total cost and the total infection risk minimized by taking the vehicle load, the road network, the time and the variables as constraints; and carrying out iterative optimization on the multi-objective optimization model to obtain optimal recovery paths and starting time in different time periods. According to the invention, through reasonable risk grading, time-varying road network modeling, risk calculation and path optimization, efficient and safe recovery of medical wastes is realized.

The embodiment of the invention belongs to the field of multi-camera systems, and relates to a multi-target area dynamic vehicle and pedestrian recognition method, which comprises the following steps: acquiring image data of a target area acquired by a plurality of cameras; preprocessing the image data, and extracting features of the target area; target detection is carried out on the image data, vehicle and pedestrian targets in the image data are identified, and the positions and features of the targets are determined; performing preliminary positioning on each target, and predicting the motion trail of the target through a Kalman filtering algorithm or a particle filtering algorithm; and carrying out space-time association on the targets identified by the plurality of cameras based on a joint data association method of the positions, the features and the motion trails of the targets. The invention also provides a multi-target area dynamic vehicle and pedestrian identification system. The coverage rate and accuracy of target identification are improved.

The invention discloses an Internet of Vehicles cooperative driving control method and system based on deep reinforcement learning, and belongs to the field of automatic drive.The method comprises the steps that state data of a main vehicle and state data of at least one adjacent vehicle received through Internet of Vehicles communication are received, generating smooth state estimation and uncertainty characteristics used for representing data credibility; constructing the main vehicle and the adjacent vehicles into a dynamic vehicle interaction diagram; inputting the dynamic vehicle interaction graph into a graph attention feature extraction model to extract space-time interaction features; inputting the space-time interaction characteristics into a deep reinforcement learning strategy network to generate a preliminary reference control instruction; a quadratic programming problem is constructed, and the quadratic programming problem takes minimization of deviation from a reference control instruction as an optimization target and takes security constraints derived from a control barrier function as constraint conditions; and solving the quadratic programming problem to obtain a final control instruction. According to the invention, efficient and intelligent cooperative driving can be realized on the premise of ensuring safety.

According to the vehicle identification and street lamp fault judgment method and the intelligent street lamp energy-saving control system, the improved European-style clustering algorithm is adopted to identify vehicles running at a high speed, and static accumulated snow and dynamic vehicles are distinguished through four stages of point cloud preprocessing, improved European-style clustering, vehicle verification and time sequence tracking, so that the false detection rate caused by rain and snow noise is reduced. A street lamp sectional type control scheme is adopted, a certain road section is divided into a plurality of lamp control subsystems, and a main control module in each lamp control subsystem controls a plurality of street lamps in the lamp control subsystem to achieve the purpose of energy conservation. Real-time monitoring of street lamp faults is realized by adopting a dynamic threshold adjustment mechanism, an alarm threshold is dynamically adjusted according to historical data, and the problem that a fixed threshold cannot adapt to load change is avoided, so that the problem of false alarm caused by power grid interference is reduced. The system can effectively deal with rain and snow weather, and can control a plurality of street lamps through one master control module, thereby greatly reducing the reconstruction cost.

The invention relates to the field of vehicle navigation, and particularly discloses a vehicle navigation method and system based on inertial navigation, and the method comprises the steps: collecting vehicle acceleration and angular velocity data through an inertial measurement unit, constructing a hypergraph structure, integrating road, environment and vehicle state nodes, mining a potential relationship of multi-source heterogeneous data by using a hypergraph neural network algorithm, and obtaining a vehicle navigation result; and designing a data resolving rule to realize data fusion analysis. On the basis, the real-time position of the vehicle is calculated, an optimal path considering dynamic factors is planned, and visual navigation information is output. The system comprises an inertial data acquisition unit, a hypergraph construction and processing unit, a multi-source heterogeneous data calculation unit and the like, all the units work cooperatively, and the navigation performance is improved through model collaborative optimization. The method gets rid of dependence on satellite signals, solves the problem of positioning in a complex environment, effectively utilizes multi-source data, and realizes accurate and dynamic vehicle navigation.

The invention relates to the technical field of intelligent scheduling, in particular to an intelligent vehicle scheduling method based on AI, and the method comprises the following steps: generating an intelligent scheduling task distribution structure table based on order sudden increase detection, vehicle path sorting, task time limit evaluation and dynamic replacement matching; according to the invention, spatial emergency demand identification is realized by extracting the geographic boundary of the abnormal aggregation order, response vehicle screening is completed in combination with the dynamic vehicle position and the road network state, and meanwhile, task execution timeliness monitoring is enhanced by introducing dual constraints of task remaining time limit and path time consumption. And then replacing vehicle matching is carried out by utilizing a standby vehicle and task point time difference evaluation mechanism, rapid scheduling and replacing of high-risk tasks are realized, optimal allocation of the overall tasks and vehicles is carried out according to an uncompleted path structure, resource mismatching and response lag are avoided, and the task scheduling efficiency is improved. And the real-time performance of scheduling decision, the rationality of path selection and the continuity of task execution are improved.

The invention relates to the technical field of intelligent cabins, in particular to a dynamic vehicle machine HMI system based on multi-modal interaction. Comprising a multi-mode biological feature acquisition module, a feature fusion identity verification module, a driving state and split screen control analysis module, a voice interaction and role instruction processing module, a driver instruction prediction module and an interaction processing module without role recognition. Wherein the driver instruction prediction module receives a historical control instruction of each driver after successful driver identity verification, constructs a historical instruction sequence, and trains an instruction prediction model corresponding to each driver; receiving a role corresponding to the output voice signal, inputting a voice vector to a corresponding instruction prediction model, and generating a prediction control instruction; and when a prediction control instruction is generated, a joint loss function is designed, so that the instruction prediction model learns a personalized instruction mode of a current driver and a common instruction mode of multiple drivers at the same time.