24842 results about "Vehicle control" patented technology

Systems and methods described herein include implementation of road surface-based localization techniques for advanced vehicle features and control methods including advanced driver assistance systems (ADAS), lane drift detection, passing guidance, bandwidth conservation and caching based on road data, vehicle speed correction, suspension and vehicle system performance tracking and control, road estimation calibration, and others.

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.

The invention provides an intelligent networked automobile dynamics simulation method and system based on a physical engine, and relates to the technical field of intelligent networked automobiles, and the method comprises the steps: obtaining vehicle parameters and road condition information of an intelligent networked automobile, optimizing a multi-agent cooperative control model through a self-adaptive optimization algorithm based on the vehicle parameters and the road condition information, and carrying out the optimization of the multi-agent cooperative control model. Constructing a digital twinborn model, and mapping the optimized multi-agent cooperative control model to an actual intelligent networked automobile control system; the method comprises the following steps: establishing a distributed edge computing network of an intelligent networked automobile group, constructing a decentralized knowledge base by utilizing a block chain technology, forming a decision-making experience data set, designing a multi-level attention mechanism to extract target information based on the decision-making experience data set and multi-modal data of a current traffic scene, and generating a final vehicle control instruction. And transmitting the final vehicle control instruction to a vehicle execution system to realize dynamic adaptive group cooperative control of the intelligent networked vehicle.

The invention relates to the technical field of unmanned vehicle control, and discloses a smart city environmental sanitation unmanned vehicle path planning and real-time monitoring method. The method comprises the following steps: firstly, acquiring a multi-source environment sensing data set such as laser radar point cloud data, a camera image sequence and real-time traffic flow information; local road network features are extracted based on laser radar point cloud data, a dynamic target motion prediction map is generated according to a camera image sequence, and real-time traffic flow information is processed to generate a regional traffic efficiency evolution curve. And inputting the data into a path optimization model to generate an initial path sequence, dividing cleaning task priorities, fusing related data and generating a final path planning scheme through a reinforcement learning algorithm. In addition, operation state data of the unmanned vehicle are collected in real time, and a path correction instruction set is generated through an anomaly detection model to update the strategy network. According to the method, the rationality and the operation efficiency of the path planning of the environmental sanitation unmanned vehicle can be improved, real-time monitoring is realized, and the operation safety and the management intelligence level are enhanced.

An agricultural vehicle may include a propulsion system, a steering system, and a vehicle controller. An agricultural vehicle may include a camera sensor having a camera field of view (CFOV), a light detection and ranging (LiDAR) sensor having a LiDAR field of view (LFOV), and an imaging controller in operable communication with the camera sensor and the LiDAR sensor. The imaging controller may include a processor and at least one non-transitory computer-readable storage medium having instructions store thereon that, when executed by the at least one processor cause the imaging controller to, at least, receive image data, segment the image data, fuse LiDAR data and the image data, create a fused instance including depth information; and provide at least the fused instance to the vehicle controller, wherein the vehicle controller is configured to provide at least one navigational command based at least partially on the fused instance.

The invention relates to the technical field of rail traffic safety detection, in particular to a railway freight train key part online monitoring system based on an unmanned aerial vehicle, which comprises an unmanned aerial vehicle control module, a multi-modal data acquisition module, an edge calculation module, a central processing module and a feedback execution module. A dynamic three-dimensional grid flight path is generated by fusing a train Beidou positioning signal and a millimeter wave radar sensing result, a system is provided with an infrared thermal imager, a laser radar and a high-speed polarization camera, bearing temperature, train body point cloud and a train coupler image sequence are obtained, and temperature rise area identification, structural deformation extraction and coupling state modeling are completed on the edge side. The central processing module outputs a multi-dimensional safety assessment result, and the feedback module generates a compensation control instruction and a graded early warning signal based on the risk fusion index. The method has the advantages of being high in autonomy degree, high in recognition precision and high in response speed, and is suitable for full-time structural intelligent inspection and early warning of the freight train in a high-speed operation environment.

The invention relates to the field of intelligent driving, in particular to a driver dangerous behavior intervention system and method based on a space-time diagram neural network, and the method comprises the steps: obtaining a physiological signal, a driving posture and control behavior data through a multi-modal data collection module; constructing a dynamic graph structure and evaluating a driver risk state by using a space-time diagram neural network risk perception engine; the intelligent intervention execution module adopts a tactile, visual or vehicle control intervention strategy according to the evaluation result; the closed-loop optimization module monitors an intervention effect and updates a risk model and a strategy; according to the system, millimeter wave radar and multi-channel visual analysis are fused, the recognition accuracy is improved to 98.6%, dangerous events are predicted 15-30 seconds in advance through physiological signals and micro-expression analysis, and sufficient response time is provided for a driver.

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.

A vehicle control and interface system configured to provide dynamic flight envelope protection of an aerial vehicle is described. In particular, the system dynamically calculates allowable trajectory limits for each axis of control through all phases of flight of an aerial vehicle and provides a visualization of the calculated limits on a graphical user interface. During normal operation, the system does not allow the operator to exceed the calculated limits. Responsive to receiving specified user input to modify vehicle navigation, however, the system causes the aircraft to enter an extended envelope by degrading vehicle parameters according to a hierarchy. Moreover, the system is configured to allow the vehicle to operate in a degraded flight state during a prolonged loss of a sensor signal by setting an adjustable hover trim position of each inceptor device and allowing an operator to control horizontal acceleration of the vehicle.

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 unmanned aerial vehicle control, and discloses an unmanned aerial vehicle inspection control system and method based on data analysis, and the method comprises the steps: receiving environment feature data of a target region, and constructing a static environment model; obtaining kinetic parameters of the unmanned aerial vehicle, and calculating a feasible flight envelope in combination with the static environment model; after taking off, the unmanned aerial vehicle collects environment data in real time, and a multi-level environment topology model is constructed; acquiring state data in real time, and judging an obstacle avoidance level; if yes, local path planning is carried out, and an optimal flight path is generated; acquiring trajectory deviation and energy consumption data, dynamically calibrating the flight envelope, and optimizing the flight envelope; according to the method, environment perception, trajectory optimization and online learning are combined, the method is adaptive to a complex environment, and the intelligent level of unmanned aerial vehicle inspection is improved.

The invention discloses a control method and system based on multi-unmanned aerial vehicle cooperative game confrontation, and belongs to the technical field of unmanned aerial vehicle control, and the method comprises the steps: S1, game environment modeling and simulation, S2, game strategy generation and optimization, S3, dynamic task allocation and cooperative control, S4, communication and information interaction optimization, and S5, efficiency evaluation and adaptive adjustment. On the basis of realizing cooperative game confrontation of multiple unmanned aerial vehicles, task allocation can be efficiently carried out on the unmanned aerial vehicles, and the autonomous decision-making capability of the unmanned aerial vehicles can be improved.

To provide a vehicle control system that can suppress a vehicle from operating even after the vehicle is brought into an operable state by an activity of an improper user.SOLUTION: Vehicle control systems (1, 1a and 1b) comprise: start-notification transmitting parts (relay servers 71 and 71a, and an operation support ECU 21a) which transmit operation start-notifications to one or a plurality of registered terminals (portable terminals 6, 6a, 6b, and 6c) associated with vehicles (2, 2a, and 2b) in accordance with operation of starting the vehicles; and operation control parts (driving support ECU 21, 21a, and 21b) which when operation control request operation is performed to the registered terminals in response to the operation-start notifications, switch the vehicles between an operation suppression state and an operation possible state.SELECTED DRAWING: Figure 6

The invention discloses an automatic driving system based on real-time holographic modeling and dynamic shielding compensation and a vehicle, and aims to solve the problem of complicated scene perception. The system comprises a real-time holographic environment modeling module for constructing a dynamic 3D environment model; the dynamic shielding compensation module outputs compensated environment data; the sensing data fusion module is used for integrating multi-source data; the high-precision positioning and mapping module is used for generating a centimeter-level environment map; the edge computing node is used for cooperatively processing fusion data; the AI self-adaptive decision making system generates a driving instruction, and the decision making efficiency is optimized and improved through a satellite auxiliary decision making unit; the V2Xcommunication module supports low-delay and high-security data interaction; the execution control unit is used for accurately controlling the vehicle; an FPGA + GPU parallel computing framework is adopted, and decision making and control are supported after modeling and compensation data are fused. According to the invention, optimal decision making and high-precision real-time control in a complex environment can be realized, and development of an automatic driving technology is promoted.

An information presentation method of presenting information to a driver of a first vehicle includes detecting an object located in front of the first vehicle. The method includes determining a risk of the objects, and outputting alert sound for alerting a first dangerous object when the first dangerous object is detected based on the risk. The first dangerous object corresponds to a first risk exceeding a predetermined level. The alert sound is presented to the driver as a sound image localized at a first position between the driver and the first dangerous object. The method includes changing a position of the sound image from the first position to a second position when the risk of the first dangerous object rises to a second risk. The second position is a position between the driver and the first dangerous object and closer to the driver than the first position.

The invention relates to the technical field of unmanned aerial vehicle control, and provides a route-free automatic inspection system, which comprises an unmanned aerial vehicle, an airborne box, an autonomous flight module, a visual acquisition module, an environment sensing module, a control instruction module and a position evaluation module, and is characterized in that the airborne box is loaded in the unmanned aerial vehicle so as to acquire environment data in the flight process of the unmanned aerial vehicle in real time; the control instruction module receives coordinates of a target area input by a user and issues the coordinates of the target area to the unmanned aerial vehicle, the environment sensing module obtains the coordinates of the target area and environment data and dynamically calculates a pre-planned path, and the position evaluation module performs evaluation according to the real-time position of the unmanned aerial vehicle and the pre-planned path to form an evaluation result. The environment sensing module is triggered to dynamically adjust a pre-planned path according to an evaluation result and adjust to form a flight route, the autonomous flight module controls the unmanned aerial vehicle according to the flight route, and the visual acquisition module identifies the state of an inspection object in real time and executes corresponding shooting or video recording operation.

The invention provides a driving behavior analysis method and system based on multi-modal sensor fusion, and the method comprises the steps: firstly obtaining a multi-source sensor data set of a target vehicle containing at least two different sensor source driving environment perception data, and carrying out the time-space alignment processing of the multi-source sensor data set, and generating a multi-source fusion data set; then, through a feature extraction layer of a preset behavior analysis network, a driving behavior feature set containing vehicle operation, environment interaction and behavior continuity features is extracted from the multi-source fusion data set, an anomaly recognition layer is used for carrying out anomaly recognition on the driving behavior feature set, and an anomaly recognition result is generated; and finally, generating driving strategy optimization data according to an abnormal recognition result, and feeding back the driving strategy optimization data to a vehicle control system to adjust a driving strategy. According to the method, driving behaviors can be comprehensively analyzed, abnormity can be timely found, the driving strategy is optimized, and the driving safety and efficiency are improved.

Described is a system for performing a simulated vehicle control task based on adversarial decision analysis. Empirical game theoretic analyses are performed between an evolving and an adversary population of neural network strategies. Each empirical game theoretic analysis includes using a neuroevolution procedure to perform a fitness-based selection of a strategy in the evolving population that out-performs the adversary population. Using an empirical game theory procedure, the neuroevolution procedure is iteratively run and the selected strategy is added to the adversary population with each iteration, resulting in monotonic strategy improvement with each iteration. Following the empirical game theoretic analyses, a final strategy is selected for the evolving population and the adversary population using a tournament selection procedure. The final strategy is used to train a neural network which is used to perform a simulated vehicle control task.

The invention provides an unmanned aerial vehicle autonomous obstacle avoidance and path planning method and system based on deep learning, and relates to the field of unmanned aerial vehicle control, and the method comprises the steps: obtaining position information and environment perception data, constructing a spatial-temporal feature matrix, extracting target motion and background feature vectors, and mapping the target motion and background feature vectors into a target-environment fusion feature field; calculating an accessibility matrix and a cost matrix to construct a track search space, generating a candidate track set and determining an optimal planned track; and performing segmented optimization on the planned track to obtain a continuous attitude sequence, and generating an adaptive control strategy. According to the invention, intelligent obstacle avoidance and efficient path planning of the unmanned aerial vehicle in a complex environment are realized.

The invention relates to the technical field of unmanned aerial vehicle track control, in particular to an unmanned aerial vehicle track control method and system. The method comprises the following steps: firstly, acquiring flight braking parameters and distribution task data of an unmanned aerial vehicle, and extracting low-altitude distribution meteorological condition feature data; then, flight state simulation is carried out based on flight braking parameters and task data, simulation quantification is carried out on flight attitude instability in combination with meteorological conditions, and then trajectory avoidance behavior control learning is carried out; and finally, constructing a trajectory control model by applying a strategy gradient algorithm, and sending the trajectory control model to an unmanned aerial vehicle control center to execute trajectory control. According to the invention, the unmanned aerial vehicle trajectory control technology is optimized, so that the unmanned aerial vehicle trajectory control technology is more perfect.

Systems and methods described herein include implementation of road surface-based localization techniques for advanced vehicle features and control methods including advanced driver assistance systems (ADAS), lane drift detection, passing guidance, bandwidth conservation and caching based on road data, vehicle speed correction, suspension and vehicle system performance tracking and control, road estimation calibration, and others.

The invention relates to the technical field of vehicle cruise control, and discloses a neural network driven vehicle adaptive cruise control method. Real-time driving data are collected through a multi-modal sensor, spatial-temporal feature fusion is carried out through a multi-layer convolutional neural network to generate dynamic environment sensing data, and a pre-trained adaptive decision model is input to obtain driving strategy parameters. And constructing a mixed integer programming model on the basis, globally planning a cruise path by adopting an incremental branch and bound algorithm integrating a dynamic relaxation threshold and a heuristic pruning strategy, and outputting optimal cruise trajectory data. A hierarchical control framework comprising a planning layer, a coordination layer and an execution layer is constructed, the planning layer generates a global trajectory sequence, the coordination layer dynamically corrects a local trajectory, and the execution layer achieves vehicle longitudinal acceleration and transverse steering angle tracking based on a robust sliding mode control algorithm and outputs a vehicle control instruction to complete self-adaptive cruise control. And the cruise control performance, safety and stability of the vehicle in a complex environment are improved.

The invention relates to the technical field of unmanned aerial vehicles, in particular to a low-power-consumption unmanned aerial vehicle concealed control and encrypted communication method and system based on BLE 5.0. According to the method, link quality is dynamically evaluated based on a BLE node detection frame, a path decision result is generated, and a self-adaptive scattering network topological structure is constructed in combination with the real-time position of an unmanned aerial vehicle and electromagnetic environment changes; packaging the unmanned aerial vehicle control instruction into a GATT feature value update message, and generating a hidden control data stream; combining time, space and channel dimensions to generate a frequency hopping sequence parameter set; performing dynamic slicing and pseudo-random recombination on the hidden control data stream through a space-time dual encryption mechanism to generate an encrypted control instruction data stream; and transmitting the encrypted control instruction data flow to an unmanned aerial vehicle execution layer through a dynamic scattering network. On the premise of keeping the low power consumption characteristic, the unmanned aerial vehicle control system has high concealment, high reliability and high safety, and meanwhile, a tracking path of an enemy to a real flying hand is cut off through the dummy flying hand layer.

A diagnostic system and method for identifying and resolving vehicle Controller Area Network (CAN) bus faults is disclosed. The system connects to a vehicle's data link connector (DLC) and automatically retrieves diagnostic trouble codes (DTCs) from multiple electronic control units (ECUs). It filters the DTCs to identify those related to CAN communication errors, groups repeated DTCs across ECUs to highlight systemic faults, and assigns repair priority based on status, scope, and criticality. A user interface displays prioritized DTCs with definitions, affected systems, possible causes, and repair guidance. Upon technician input, the system re-scans the network to confirm resolution and updates the display accordingly. The invention supports semantic and conceptual recognition of communication faults, enabling robust, language-agnostic analysis. This system reduces diagnostic time, improves repair accuracy, and supports multilingual or manufacturer-specific DTC definitions without requiring external infrastructure or advanced user expertise.

The invention belongs to the field of road vehicle control, and relates to an automatic driving vehicle collision risk prediction method for a long-tail phenomenon, which comprises the following steps of: screening high-risk track samples, and realizing high-fidelity modeling and synthesis of rare high-risk driving behaviors by adopting a track generation method fusing a diffusion probability model and a Transformer structure. Therefore, the learning ability of the model to the long-tail sample is enhanced; besides, in consideration of the fact that in a long-tail scene, the kinematics characteristics and the traffic flow density characteristics of the vehicles generally have significant fluctuations, the kinematics characteristics and the traffic flow density characteristics of the surrounding traffic participants are constructed and extracted, and the future trajectories of the surrounding vehicles are predicted through multi-modal characteristic fusion; on the basis, a probability risk assessment mechanism for different risk scenes is constructed, the potential collision risk or behavior anomaly probability is dynamically assessed, and finally refined prediction of potential high-risk events in the complex traffic environment is achieved.

The invention discloses an efficient flight control system of a low-altitude manned unmanned aerial vehicle based on tilting composite wings, and relates to the technical field of unmanned aerial vehicle control systems. The low-altitude manned unmanned aerial vehicle efficient flight control system based on the tilting composite wings comprises an initialization and pre-check module, a vertical take-off and landing control module, a transition flight control module, an obstacle avoidance and path planning module and a landing control module. Through integration of vertical take-off and landing, smooth transition to cruise, intelligent obstacle avoidance, path planning and other modules, autonomous and efficient flight in a complex environment is realized, and the application scene of the unmanned aerial vehicle is greatly widened. Meanwhile, the accurate flight control and data post-processing analysis capability not only guarantees the safety and comfort of passengers, but also promotes the continuous optimization of the system performance through the real-time performance evaluation and feedback mechanism. In addition, the aerial refueling / charging technology is supported, the cruising ability is prolonged, and the task execution continuity and flexibility are enhanced.

The invention relates to the technical field of unmanned aerial vehicle control, in particular to an unmanned aerial vehicle self-adaptive navigation system which comprises an inertial navigation system, a visible light sensor, a data fusion module, a database and a navigation control module. The inertial navigation system provides attitude, speed and acceleration data; the visible light sensor collects an image and obtains environment feature points through deep learning algorithm processing to realize visual positioning; and the data fusion module fuses two kinds of sensor data by using a dynamic weighted Kalman filtering algorithm, and dynamically adjusts the weight according to the environment to optimize the positioning information. The map library and the feature library store environment data to assist in positioning and dynamically update; the navigation control module generates an instruction according to the fused positioning information, and automatically adjusts a flight strategy by considering various factors based on an adaptive control algorithm. The system improves the positioning precision and environmental adaptability, enhances the flight safety and stability, is high in intelligence and autonomy degree, has flexibility and expandability, and can be widely applied to the field of unmanned aerial vehicles.

The invention relates to the technical field of intelligent vehicle control, in particular to a loader vehicle control method and system, computer equipment and a storage medium. The method comprises the following steps: collecting multi-modal data of the loader, wherein the multi-modal data comprises working environment data, dynamic load state data and vehicle attitude data of the loader; constructing a three-dimensional operation scene digital twinborn model based on the multi-modal data, and predicting vehicle stability influence data of the influence of the load change on the vehicle stability based on the dynamic load state data; based on the digital twinborn model of the three-dimensional operation scene and the vehicle stability influence data, a dynamic control strategy is generated by utilizing a reinforcement learning algorithm, and cooperative actions of steering, lifting and walking mechanisms are synchronously optimized; according to operation environment data and a dynamic control strategy, hydraulic system pressure and motor torque distribution are adjusted in a self-adaptive mode, and operation efficiency and safety are ensured.

The invention discloses a vehicle seat self-adaptive adjusting method and system based on environment perception and a medium. The method comprises the steps that physiological state data and human body image data of a target passenger and cabin environment data of a target cabin are obtained; determining body surface temperature and humidity, heart rate parameters and human body metabolic rate of the target passenger according to the physiological state data, and identifying clothing thermal resistance parameters of the target passenger according to the human body image data; determining a human body thermal comfort index of the target cabin according to the human body metabolic rate, the clothing thermal resistance parameter and the cabin environment data; inputting the body surface temperature and humidity, the heart rate parameter and the human body thermal comfort index into a pre-trained thermal comfort demand prediction model to obtain a target thermal comfort demand of the target passenger; and performing heating adjustment and / or ventilation adjustment on the seat of the target cabin according to the target thermal comfort demand. The riding comfort and riding experience of the user are improved, and the method can be widely applied to the technical field of vehicle control.

A vehicle control and interface system described herein assists an operator of an aerial vehicle with the operation of an aerial vehicle, including automated control of the aerial vehicle during flight. The system can generate a graphical user interface (GUI) on which lateral guidance and vertical guidance initiation elements are displayed. The system can conditionally disable the vertical guidance initiation element from operator interaction until at least the operator interacts with the lateral guidance initiation element (e.g., to prevent an undesirable roll maneuver during descent). If the operator interacts with the vertical guidance initiation element before engaging lateral guidance, the aerial vehicle may maintain a current altitude rather than climb or descend according to a target vertical guidance route. In this way, the GUI reformats elements based on the operational state of the aerial vehicle and reduces a risk of operational error.