2465 results about "Vehicle dynamics" patented technology

The invention provides an intelligent traffic decision-making method based on multi-source sensor data, and the method comprises the steps: obtaining vehicle sensor data and road information, and obtaining a standardized traffic flow feature vector through data synchronization, fusion, feature extraction and dimension reduction processing; inputting the standardized traffic flow feature vector into a traffic flow prediction model, and predicting the traffic flow through a vehicle dynamics model constraint state transition matrix; according to the predicted traffic flow and the real-time road condition information, the road congestion state is judged, and a real-time congestion map is generated; in combination with the real-time congestion map, the road network speed limit rule and the starting and ending point position information, an optimal path is planned, and path smoothing processing is performed through a driving behavior mode; and constructing a vehicle intelligent decision-making system, integrating traffic flow prediction, congestion detection and a path planning model, and outputting a vehicle driving strategy according to real-time vehicle sensor data.

The invention relates to the technical field of fault detection control, and discloses an automobile fault detection method and system fusing sensor data. The method comprises the following steps: modeling a multi-system sensor network through an automobile system topology mapping algorithm to obtain a sensor topology incidence matrix; performing synchronous processing on the sensor topological incidence matrix according to a vehicle dynamics constraint equation to obtain a synchronous multi-sensor data set; inputting the synchronous multi-sensor data set into a fault propagation path sensing network for fusion to obtain a vehicle system fault feature vector; detecting the fault feature vector based on an automobile physical model constraint optimizer to obtain a fault state recognition result; and tracing and controlling a fault state identification result according to the fault propagation atlas model to obtain a system adjustment control signal. According to the invention, the source identification accuracy of the complex coupling fault and the pertinence of control and adjustment are improved.

Disclosed in the present invention are a multi-agent vehicle-road-cloud integrated collaborative decision-making and control architecture system and method based on federated reinforcement learning. A multi-agent federated reinforcement learning decision-making and control framework having embedded vehicle dynamics characteristics is used, so as to solve the problem of in-depth integration of an intelligent traffic system and intelligent vehicles, and realize autonomous driving with vehicle-traffic in-depth decision-making and control collaboration; a semantic matrix is generated at a road side to serve as an input for vehicle-side reinforcement learning, so as to construct vehicle-side global and local trajectory planning guided by the road side; an integrated reward function for vehicle-side reinforcement learning is designed on the basis of a driving safety field constructed by the road side, so as to realize comprehensive consideration of vehicle-side safety and comfort; on the basis of road-side federated learning, vehicle-side neural network parameters are uploaded by means of V2I communication, so as to solve the problem of vehicle-road information asymmetry caused by privacy awareness; and for different environmental sample distributions, a local optimal policy for a current environment is selected by means of neural network screening, so as to synthesize a shared model benefiting from different environments, thus realizing a balance between sample efficiency and model robustness.

The invention provides a vehicle driving track prediction method, system and device and a medium, and belongs to the technical field of vehicle track prediction.The method comprises the steps that vehicle state information and environment information of each track point of a vehicle are collected to serve as track data, and a deep learning denoising algorithm is used for preprocessing and feature sequence extraction; constructing a trajectory prediction model by using a Transform in combination with a graph neural network, constructing a training set by using the extracted feature sequence to train the trajectory prediction model, and adjusting model parameters by using a multi-task loss function constructed by trajectory prediction precision and uncertainty estimation in the training process; and using the trajectory prediction model to predict the vehicle trajectory, using the Bayesian optimization method to optimize the trajectory parameters with the goal of minimizing the prediction error, and adjusting the optimization parameters with the vehicle dynamics constraint and the road constraint as constraint conditions in the optimization process. According to the invention, through multi-fusion and multi-stage optimization, high-precision prediction of the vehicle track in a complex traffic environment is realized.

The invention discloses a mountainous area icy road vehicle driving speed early warning method based on physical information constraint, and relates to the technical field of intelligent traffic and vehicle safety, and the method comprises the steps: carrying out the multi-modal data collection and preprocessing, constructing a multi-modal feature extraction network to extract a high-dimensional vector, constructing a fusion module based on an attention mechanism to output a fusion feature vector, and carrying out the early warning of the driving speed of a mountainous area icy road. The method has the advantages that data such as environment, vehicle dynamics and road surface friction coefficients are collected through the multi-source sensor fusion technology, a multi-modal feature extraction and fusion network is constructed, and the real-time classification early warning is realized through the multi-modal feature extraction and fusion network; the method comprises environment, vehicle dynamic and road friction feature extraction sub-networks, deep extraction of different modal data features, a fusion network based on an attention mechanism, concerning of association, coupling and cooperative influence among data, and dynamic weighting of fusion features, so that the purposes of accurately sensing a complex environment, reducing false alarm and missing alarm and effectively improving early warning accuracy are achieved.

The invention discloses an intelligent engineering vehicle scheduling method based on artificial intelligence, and the method comprises the steps: constructing a scheduling objective function which at least comprises the engineering vehicle transportation cost, the task completion time cost, the path congestion cost and the energy consumption cost; establishing a multi-dimensional task demand prediction model based on historical task data and real-time traffic data, and outputting a task distribution prediction value and a traffic state prediction value in a future time period; inputting the model into an engineering vehicle dynamic scheduling model established based on a deep reinforcement learning algorithm for iterative optimization; and generating a real-time scheduling instruction based on the optimized dynamic scheduling model, dynamically allocating task paths and resources of the engineering vehicle, and monitoring an execution state in real time to adjust a scheduling strategy. According to the method, efficient, economical and environment-friendly intelligent scheduling of the engineering vehicle is realized through construction of a scheduling objective function, multi-dimensional demand prediction, deep reinforcement learning dynamic optimization and real-time scheduling and monitoring.

The invention relates to the technical field of vehicle dynamics control, and discloses a vehicle control method and system for a double-closed-loop adjustable magneto-rheological semi-active suspension, and the method comprises the steps: S1, obtaining the state information of a vehicle based on a sensor, building a vibration control model, and calculating and outputting the expected damping force of vibration control; s2, establishing a vehicle body attitude control model, and calculating and outputting vehicle body attitude control expected damping force; s3, calculating the expected damping force obtained in the step S1 and the expected damping force obtained in the step S2 to obtain the total expected damping force; and S4, according to the total expected damping force, the relative speed of the wheels and the vehicle body and the dynamic deflection information of the suspension, a suspension inverse model is established, and expected current is output. According to the method, the control system is optimized by combining the design parameters of the shock absorber and the test parameters of the real vehicle, the current loop adjustment is designed on the basis of the damping loop control, and the tracking compensation control of the current is mainly realized, so that the response speed and the accuracy of the control are improved, and the performance of the magneto-rheological semi-active suspension is further improved.

The invention discloses an unmanned vehicle intelligent obstacle avoidance method and system based on a multi-modal sensor, and relates to the field of automatic driving and intelligent traffic. According to the technical key points of the invention, data of a laser radar and a depth camera are aligned through a space-time registration module; the laser radar point cloud is preprocessed, obstacle point cloud is segmented, fusion clustering is carried out in combination with three-dimensional semantic seed points output by a depth camera, and an obstacle cluster with a semantic tag is generated; and calculating a three-dimensional directed bounding box (OBB) of an obstacle, dynamically adjusting a safety distance according to the speed of the vehicle, expanding the OBB of the vehicle, and carrying out collision detection by adopting a separation axis theorem. When a collision risk is detected, the system sequentially triggers a first-level early warning instruction and a second-level emergency braking instruction, and safe and efficient unmanned vehicle dynamic obstacle avoidance is achieved. According to the method, the advantages of multiple sensors are fully utilized, the obstacle detection precision and the response speed are effectively improved, and reliable technical support is provided for application of the unmanned driving technology in a complex road environment.

The invention relates to the technical field of automatic driving, and particularly provides an automatic driving vehicle track re-planning method, which comprises the following steps of: firstly, evaluating stability risk and defining three states of vehicle stability through real-time vehicle dynamic parameters, and designing intervention and exit criteria of track re-planning according to the three states; when the vehicle driving is evaluated to be in an unstable state, generating a virtual obstacle by using prediction information of a path tracking module, and converting a dynamic stability boundary into a spatial constraint; performing trajectory re-planning by adopting nonlinear model predictive control, and adjusting the weight between the path tracking precision and the obstacle avoidance demand according to the relative position of the virtual obstacle in combination with a dynamic weight adjustment mechanism; and finally, generating an optimized trajectory containing stability constraint, and realizing high-precision tracking through linear MPC. The method can effectively balance the path tracking precision and the vehicle stability under a complex working condition, and prevents the vehicle from entering a dynamic unstable region.

The invention relates to the technical field of forklift driving stability control, in particular to a forklift driving stability control system based on ultrasonic and laser composite ranging, which comprises a composite ranging module, a multi-modal data fusion module, a stability prediction and control module, an execution mechanism control module and an online learning system. The composite distance measuring module collects surrounding environment information and corrects motion errors through a dynamic compensation algorithm; the multi-modal data fusion module dynamically adjusts the weight of the sensor based on an environmental interference factor, and realizes space-time calibration in combination with extended Kalman filtering; the stability prediction and control module is fused with the vehicle dynamic characteristics to predict the rollover risk and generate a hierarchical control instruction; the actuating mechanism control module drives actuating components such as an active suspension and an electronic differential mechanism through a layered framework. The method can adapt to complex working conditions, the prediction precision and fault-tolerant capability are improved, safety and efficiency are balanced, the maintenance cost is reduced, and the method is suitable for various forklifts.

The invention relates to a vehicle brake-by-wire method and system, and relates to the technical field of brake-by-wire, and the method comprises the steps: obtaining environment information around a vehicle and vehicle attitude information; identifying road conditions based on the environment information and obtaining dynamic parameters of the vehicle based on the vehicle attitude information and a vehicle dynamics model; obtaining a vehicle state evaluation result based on the identified road condition and the vehicle dynamic parameters; the vehicle state evaluation result is obtained based on the driving state and the potential risk of the vehicle under the current road condition; determining a braking force distribution proportion and an adjustment amount of each wheel by using a braking force distribution matrix and a braking force adjustment algorithm based on the vehicle state evaluation result; and adjusting the brake fluid pressure of each wheel based on the determined brake force distribution ratio and adjustment amount of each wheel.

The invention discloses an expressway in-transit vehicle dynamic behavior analysis method and device and a medium, and belongs to the field of electric digital data processing. The method comprises the following steps: preprocessing multivariate data collected in real time, and fusing the preprocessed multivariate data to form original path information of a vehicle; on the basis of a road network topological structure and a shortest path algorithm, original path information is fitted, and a continuous and complete vehicle driving track is generated; based on the vehicle driving track, comparing the multivariate data in real time, determining whether the vehicle has an abnormal behavior, if yes, continuing to judge an abnormal type corresponding to the vehicle, and generating early warning information corresponding to the abnormal type; and according to the multivariate data of the to-be-predicted road section in the expressway, predicting the traffic flow of the to-be-predicted road section in real time, and displaying the traffic flow and early warning information of the to-be-predicted road section through a user interaction interface, thereby realizing dynamic behavior analysis of the in-transit vehicles on the expressway.

The invention provides an unmanned aerial vehicle bionic group trajectory emergence method and system of a space-time gradient field, and relates to the technical field of unmanned aerial vehicle group collaborative navigation and adaptive control, and the method comprises the steps: generating a four-dimensional space-time gradient field tensor matrix through a dynamic field coupling convolutional network; in combination with density-speed response data of fish school escape behaviors and unmanned aerial vehicle dynamics constraints, a bionic group interaction rule set is constructed through nonlinear differential game modeling; adopting a space-time finite element discretization method and combining with GPU acceleration parallel solving to determine an individual trajectory basis function of the unmanned aerial vehicle; performing optimization processing through a Lyapunov function to generate a group recombination instruction; and generating a space-time gradient field enabled unmanned aerial vehicle bionic group trajectory emergence scheme by integrating the updated space-time gradient field, the group recombination instruction and the historical trajectory data. According to the invention, high-precision, low-delay and high-robustness autonomous navigation capability is provided for the unmanned aerial vehicle group, and the task success rate is improved.

The invention discloses a sliding-mode adaptive vehicle roll stability control method based on deep reinforcement learning, which is applied to an active suspension vehicle and comprises the following steps: establishing a six-degree-of-freedom steering-roll vehicle dynamics model by combining a Doguff tire model, and calculating a target vehicle body roll angle based on a reverse roll mechanism. A sliding mode controller comprising a linear sliding mode surface and an exponential approaching rate is designed, an intelligent agent based on a soft behavior strategy gradient algorithm (SAC) is constructed, and the intelligent agent adaptively outputs optimal sliding mode parameters by sensing the vehicle state and the roll angle tracking condition. And finally, the sliding mode controller combines the optimal sliding mode parameters and the vehicle state to calculate the active roll-over moment and distributes the active roll-over moment to the main power of the left and right side suspensions, so that the precise regulation and control of the vehicle body posture are realized. According to the method, the roll stability domain during high-speed steering can be remarkably expanded, the lateral sensing acceleration of passengers is reduced, the high-robustness tracking effect is kept under complex disturbance, and meanwhile sliding mode buffeting is restrained.

The invention discloses a law and regulation simulation test method, device and equipment for an intelligent driving algorithm and a storage medium, and relates to the technical field of intelligent driving, and the method comprises the steps: carrying out the high-precision three-dimensional reconstruction of a law and regulation test site according to collected data, and obtaining a three-dimensional reconstruction scene, and the collected data comprises a laser radar point cloud and a multi-view camera image; obtaining simulation working condition test data based on the three-dimensional reconstruction scene; training a network model according to the simulation working condition test data and the real vehicle working condition test data to obtain a vehicle dynamics network model; and based on an automatic test program, constructing a closed-loop simulation link according to the three-dimensional reconstruction scene and the vehicle dynamics network model, and carrying out regulation simulation test on an intelligent driving algorithm. According to the invention, the efficiency and accuracy of intelligent driving regulation testing can be improved.

The invention belongs to the technical field of path planning, and discloses a path planning method and system fusing multi-modal terrain perception and vehicle dynamics, and the method comprises the steps: obtaining and fusing multi-modal geographic data to generate a terrain physical representation, and calculating the terrain complexity based on the terrain physical representation; predicting an overturning index and a stability margin of the vehicle based on the terrain physical representation, and generating a trafficability cost function; taking the trafficability cost function as a constraint condition, selecting a motion element according to terrain complexity to perform trajectory fitting, and generating a vehicle path; executing the vehicle path to obtain a vehicle state, correcting the trafficability cost function by using the vehicle state, and optimizing the vehicle path; and constructing a loss function, training the path planning model, and performing path planning by using the trained model. According to the invention, by establishing a terrain vehicle bidirectional coupling mechanism, safe and adaptive local path planning under a complex unstructured terrain is realized, and the traffic capacity and safety of the system in a dynamic environment are significantly improved.

The invention discloses an automatic driving control method based on passenger motion sickness perception and comfort, which comprises the following steps: acquiring and preprocessing vehicle dynamic performance data, passenger physiological signals and subjective scores, extracting dynamic characteristics such as linear acceleration, angular velocity, jerk and omnidirectional acceleration, and physiological characteristics such as electrocardio and galvanic skin; feature fusion and standardization are carried out; a passenger motion sickness perception and comfort score prediction model is constructed; an individualized motion sickness threshold value is constructed based on a model output result, a motion sickness risk score is calculated in combination with future short-time vehicle motion characteristics, and perspective estimation of the motion sickness risk is achieved; and executing a hierarchical processing strategy according to the sensitivity typing and the risk level, and dynamically adjusting the vehicle driving style, the sound-light-temperature environment in the vehicle and the voice prompt. According to the automatic driving control method based on the motion sickness sensing and comfort of the passenger, real-time identification and active intervention of the motion sickness state of the passenger can be achieved, and the riding comfort and the man-machine cooperation level of an automatic driving vehicle are improved.

The invention discloses a chassis coordination control method and system based on tire side deviation state evaluation, and relates to the technical field of four-wheel steering four-wheel drive vehicle dynamics control. The method comprises the steps that an expected front wheel rotation angle is determined based on an expected steering wheel rotation angle input by a driver, and a front wheel rotation angle is determined; further determining a feed-forward front wheel steering angle, a feed-forward rear wheel steering angle and an additional yaw moment based on feed-forward control and feedback control, determining a first tire slip angle and a second tire slip angle based on a road adhesion coefficient, and further determining a wheel torque control weight, a feedback front wheel steering angle control weight and a feedback rear wheel steering angle control weight; and further determining a driving yaw moment, a feedback front wheel steering angle and a feedback rear wheel steering angle, determining a total longitudinal force based on an expected longitudinal acceleration input by a driver, and performing four-wheel torque distribution based on the total longitudinal force and the driving yaw moment to obtain a four-wheel driving torque. The driving stability of the vehicle under the limiting working condition can be improved.

The invention relates to a vehicle queue cooperative control method fusing deep reinforcement learning communication time delay compensation, and belongs to the technical field of intelligent vehicle control. For the problems of control error accumulation and queue instability caused by communication time delay in the vehicle-road cloud cooperative system, a solution integrating deep reinforcement learning prediction and preset performance control is provided. According to the technical scheme, the method comprises the following steps: constructing a vehicle state prediction model based on a DDPG algorithm, and generating a time delay compensation amount in real time; establishing a vehicle dynamics model containing communication time delay, and designing a sliding mode controller based on a barrier function; and dynamically adjusting the control constraint in combination with an adaptive error boundary mechanism. According to the method, through closed-loop fusion of prediction compensation and dynamic constraint, under the scene of burst communication delay (100-400 ms), the queue spacing error is reduced by 62%, the speed tracking error is still kept to be smaller than 0.3 m / s, and the queue stability and the anti-jamming capability are remarkably improved.

The invention discloses a distributed driving electric vehicle path tracking control method, system and device, and relates to the technical field of vehicle chassis control. The method comprises the steps that vehicle parameter information is received, a vehicle dynamics model and a magic formula tire model are constructed according to the vehicle parameter information, and a vehicle-road system model used for trajectory tracking control is constructed based on the vehicle dynamics model and the magic formula tire model. According to the method, various uncertain disturbances in the vehicle driving process are comprehensively considered, a model predictive control framework based on minimum-maximum robustness is designed, and the robustness of a path tracking control system is enhanced; meanwhile, an online observer based on a minimum robust positive invariant set is designed, real-time and accurate obtaining of key parameters of the vehicle is achieved without increasing hardware cost, meanwhile, an active front wheel steering and torque vector cooperative control method is designed, and coordinated operation between a steering system and a four-wheel drive system is achieved.

The invention provides a distributed steer-by-wire vehicle stability control method. The method comprises the steps that a distributed steer-by-wire vehicle dynamics model, a two-degree-of-freedom reference model and a four-wheel steering angle distribution model are established; in the design of the yaw stability controller, a hybrid sensitivity # imgabs0 # robust control method is adopted, the uncertainty of the system is fully considered, and the robustness of the system can be considered while the target tracking performance is ensured.

The invention provides a path collaborative planning method for a hybrid vehicle group conflict area, and belongs to the technical field of vehicle path planning. According to the method, the virtual following target point of the driver is obtained by sampling the speed of the manual driving vehicle and the deviation path distance relative to the road reference line to generate the sampling track, and the driving behavior of the driver in the unstructured road environment is predicted; a forward deduction method of vehicle dynamics is combined to predict a short-time trajectory of the manually driven vehicle as a reference trajectory to evaluate the confidence coefficient of the reference trajectory, so that the accuracy of the sampling trajectory can be improved, and the collaboration and safety among the vehicles are improved; besides, in combination with information of the space-time risk field, the priority and the driving track of the unmanned vehicle are dynamically adjusted by adopting a space-time hybrid A star algorithm based on a dynamic risk field, so that efficient dynamic redistribution of space-time resources in a conflict area scene is realized, and the collision risk of the vehicle in the conflict area is reduced. According to the method, the multi-vehicle cooperative operation stability in the mixed scene can be remarkably improved.

The invention belongs to the technical field of vehicle dynamics, and relates to an automatic driving vehicle dynamics parameter adaptive identification method based on SQP and GRNN, and the method comprises four parts: automatic driving vehicle mechanism model establishment, GRNN model establishment, training set acquisition based on the SQP algorithm, and parameter adaptive identification based on the trained GRNN model. Kinetic model key parameters which are difficult to obtain by a conventional method are solved in an off-line mode by using an automatic driving automobile mechanism model and an SQP algorithm, and a solving result is used as a training set of a GRNN (Generalized Recurrent Neural Network) model; and the trained GRNN model has the capability of processing the nonlinear relationship among the steering wheel angle, the vehicle speed, the tire cornering stiffness, the suspension roll stiffness and the roll damping in the automatic driving vehicle mechanism model. Finally, accurate kinetic parameters are identified in real time according to the operation condition of the vehicle, and a foundation is laid for effective control of the automatic driving vehicle.

The invention belongs to the field of traffic control, and relates to a high-risk scene-oriented risk, dynamics and intention collaborative trajectory prediction method, which comprehensively utilizes an outward risk clue, vehicle dynamics characteristics and driving intention information (historical trajectory characteristics), and constructs a priori enhanced risk attention mechanism and a three-body interaction micrograph module to predict the risk, dynamics and intention collaborative trajectory of a high-risk scene. Early perception of potential risks is realized; meanwhile, a multi-scale dynamic time sequence encoder is adopted to capture the dynamic change of the vehicle from short-term disturbance to long-term behavior trend; an explicit intention recognition auxiliary task is introduced, and semantic constraint and optimization are carried out on the multi-modal trajectory; through collaborative modeling of the modules, the trajectory prediction precision and robustness of the automatic driving system under complex high-risk situations such as sudden cut-in and sudden brake can be remarkably improved, more reliable prior support is provided for a subsequent decision and control module, and therefore the driving safety of a vehicle in a complex traffic environment is effectively improved.

The invention provides a distributed electric drive vehicle four-wheel differential torque enhanced steering control method and system, and belongs to the technical field of artificial intelligence risk prediction. The vehicle is provided with an upper-layer torque decision module, a lower-layer torque cooperation module and a vehicle-mounted sensor; selecting a pivot steering auxiliary strategy according to an actual driving scene, and pressing a function key to enter a working condition; vehicle dynamic information is obtained through a vehicle-mounted sensor, whether a vehicle is in an in-situ steering state or a small-radius steering state is judged by combining vehicle body static data, and when a selected function key is in an in-situ steering state or a small-radius steering state, the vehicle dynamic information is obtained in real time, and fuzzy reasoning and a neural network algorithm are combined. The ground attachment coefficient and the target yaw angle speed are intelligently judged, and therefore accurate control over the steering torque is achieved; or a double-controller structure is adopted, the torque of each wheel can be effectively distributed and adjusted under complex working conditions, and flexible control and stable running of the vehicle in a narrow space are ensured.

The invention relates to a vehicle dynamic adjustment method for a side collision accident, and the method comprises the following steps: S1, collecting vehicle body multi-dimensional information data information, vehicle environment information data information and vehicle passenger information data information, and building a real-time updated data model through data fusion; training a neural network according to virtual collision data generated by the vehicle collision simulation software to obtain a collision risk prediction model; s2, inputting data in the model into a collision risk prediction model to obtain the injury degree of a passenger and collision prediction position point information, dividing the state between a vehicle and a potential collision object into different risk levels, and generating a risk map; and S3, the vehicle adjusting system adjusts the height, the speed and the seat posture of a vehicle suspension system and a vehicle safety belt according to the different risk levels, risk prompting is carried out on vehicle passengers so as to reduce the risk levels, and the risk map is updated in real time. According to the invention, the safety of passengers in a side collision accident can be improved.

The invention discloses a distributed intelligent control method for unmanned aerial vehicle cluster cooperative path planning, and relates to the technical field of unmanned aerial vehicle cluster cooperative control, and the method comprises the steps: collecting a dynamic feature set of an unmanned aerial vehicle in real time, and carrying out the time synchronization, noise filtering and formatting integration, and forming a local state data set; performing neighborhood collaborative analysis and temporary link planning on the local state data set to obtain relay chain layout data; performing environmental risk assessment and path feasibility analysis on the relay chain layout data to generate a candidate path set; adjusting the candidate path set through local obstacle avoidance and dynamic constraint to generate an executable path sequence, and generating a collaborative execution path through neighborhood communication and collaborative adjustment; performing task execution result integration and state updating on the collaborative execution path to generate group state data; and performing path feedback and loop optimization on the group state data to obtain a group collaboration result. And the task completion rate, the cooperation efficiency and the environment adaptability are improved.

The invention belongs to the technical field of reusable aircraft control, and relates to an intelligent fault diagnosis and control method for a reusable aircraft. According to the method, a control-oriented aircraft dynamics model is established, a neural network adaptive fault observer and a particle swarm optimization algorithm are fused, and high-precision tracking control over the attitude and trajectory of the aircraft is achieved. When faults such as performance loss of an actuator and unmeasurable deviation occur, fault information can be estimated quickly and accurately, the reconstructed fault information is integrated into control law design, and it is guaranteed that the aircraft still keeps stable flight in a serious fault state. Meanwhile, the method can effectively deal with composite disturbances such as pneumatic uncertainty, elastic modal coupling and external environment interference, and improves the real-time performance and accuracy of fault diagnosis by means of high-precision joint online estimation. In addition, the particle swarm optimization algorithm further enhances the overall performance and adaptive ability of the control system, so that the aircraft operates more efficiently and stably in the flight process.

The invention discloses a light passenger car chassis steering stability self-adaptive control method, which comprises the following steps of: collecting multi-dimensional working condition parameters in real time, and covering 12 types of key parameters such as a steering wheel rotating angle and a wheel speed; after preprocessing and abnormity diagnosis, double models are adopted to estimate the dynamic state of the vehicle in parallel; dynamically setting a control target, and realizing personalized control in combination with driver style recognition; the control quantity is optimized through multi-mode strategy dynamic switching and steering and braking system cooperative control; and actuator dynamic compensation, scenarized self-learning and multi-stage fault handling mechanisms are introduced. According to the method, the complex working condition adaptability is improved, the response speed is controlled, the subsystem cooperation efficiency is improved, the system robustness and the long-term use performance are enhanced, and the driving safety of the light bus is effectively guaranteed.

The invention discloses a trajectory optimization method and device and a perceptual model training method and device. The trajectory optimization method comprises the following steps: acquiring multi-view image data acquired by a vehicle at the current moment and current vehicle state information; on the basis of the multi-view image data, corridor prediction and trajectory prediction are carried out through a pre-trained perception model, and predicted safe corridor information and predicted trajectory information of the vehicle within a predicted duration are obtained; and based on the predicted safety corridor information, the predicted trajectory information, the current vehicle state information and trajectory optimization conditions, optimizing the predicted trajectory information to obtain optimized predicted trajectory information. By adopting the method and the device, the track can be optimized through the sensing model on the premise of meeting the safety constraint and the vehicle dynamics limitation, so that the collision risk is reduced.